Publications by TILOS Faculty

Nikolay Atanasov

  1. X. Liu, J. Lei, A. Prabhu, Y. Tao, I. Spasojevic, P. Chaudhari, N. Atanasov and V. Kumar, "SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation," preprint (arXiv:2406.17249), 2024.
  2. T. Wang, D. Bhatt, X. Wang and N. Atanasov, "Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignment," preprint (arXiv:2406.01968), 2024.
  3. T. Duong, A. Altawaitan, J. Stanley and N. Atanasov, "Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control," preprint (arXiv:2401.09520), 2024. (Submitted to IEEE Transactions on Robotics.)
  4. K. Long, Y. Yi, Z. Dai, S. Herbert, J. Cortés and N. Atanasov, "Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments," preprint (arXiv:2405.18251), 2024.
  5. K. Long, K. Tran, M. Leok and N. Atanasov, "Safe Stabilizing Control for Polygonal Robots in Dynamic Elliptical Environments," preprint (arXiv:2310.00273), 2024. (To appear at the 2024 American Control Conference.)
  6. K. Long, J. Cortes and N. Atanasov, "Distributionally Robust Policy and Lyapunov-Certificate Learning," preprint (arXiv:2404.03017), 2024. (Submitted to IEEE Open Journal of Control Systems.)
  7. C. Nguyen, A. Altawaitan, T. Duong, N. Atanasov and Q. Nguyen, "Adaptive-Frequency Model Learning and Predictive Control for Dynamic Maneuvers on Legged Robots," preprint (arXiv:2407.14749), 2024. (Submitted to IEEE.)
  8. V. Duruisseaux, T. P. Duong, M. Leok and N. Atanasov, "Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems," Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023. [Link]
  9. P. Mestres, K. Long, N. Atanasov and J. Cortés, "Feasibility Analysis and Regularity Characterization of Distributionally Robust Safe Stabilizing Controllers," IEEE Control Systems Letters, 2023. [Link]
  10. K. Long, Y. Yi, J. Cortes and N. Atanasov, "Distributionally Robust Lyapunov Function Search Under Uncertainty," Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023. [Link]
  11. E. Sebastian, T. Duong, N. Atanasov, E. Montijano and C. Sagués, "Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks," IEEE International Symposium on Multi-Robot & Multi-Agent Systems, 2023. [Link]
  12. E. Sebastian, T. Duong, N. Atanasov, E. Montijano and C. Sagués, "LEMURS: Learning distributed multi-robot interactions," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Link]
  13. E. Sebastian, T. Duong, N. Atanasov, E. Montijano and C. Sagues, "Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems," preprint (arXiv:2401.00212), 2023. (Submitted to IEEE Transactions on Robotics.)
  14. A. Altawaitan, J. Stanley, S. Ghosal, T. Duong and N. Atanasov, "Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control," preprint (arXiv:2309.09163), 2023.
  15. Z. Li, T. Duong and N. Atanasov, "Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics," IEEE Open Journal of Control Systems, 2022. [Link]
  16. S. W. Chen, T. Wang, N. Atanasov, V. Kumar and M. Morari, "Large scale model predictive control with neural networks and primal active sets," Automatica, 2022. [Link]
  17. T. Duong and N. Atanasov, "Hamiltonian-based neural ODE networks on the SE(3) manifold for dynamics learning and control," Robotics: Science and Systems, 2021. [Link]
  18. M. Shan, Q. Feng, Y.-Y. Jau and N. Atanasov, "ELLIPSDF: Joint object pose and shape optimization with a bi-level ellipsoid and signed distance function description," Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. [Link]
  19. P. Paritosh, N. Atanasov and S. Martinez, "Marginal density averaging for distributed node localization from local edge measurements," 2020 59th IEEE Conference on Decision and Control (CDC), 2020. [Link]
  20. M. Shan, Q. Feng and N. Atanasov, "OrcVIO: Object residual constrained visual-inertial odometry," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. [Link]
  21. E. Zobeidi, A. Koppel and N. Atanasov, "Dense incremental metric-semantic mapping via sparse gaussian process regression," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. [Link]
  22. B. Schlotfeldt, D. Thakur, N. Atanasov, V. Kumar and G. J. Pappas, "Anytime planning for decentralized multirobot active information gathering," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1025-1032, 2018. [Link]
  23. S. L. Bowman, N. Atanasov, K. Daniilidis and G. J. Pappas, "Probabilistic data association for semantic SLAM," 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017. [Link]

Esmaeil Atashpaz-Gargari

  1. E. Atashpaz-Gargari, M. S. Reis, U. M. Braga-Neto, J. Barrera and E. R. Dougherty, "A fast Branch-and-Bound algorithm for U-curve feature selection," Pattern Recognition, 2018. [Link]
  2. E. Atashpaz-Gargari, "Smooth Optimal Control for a Class of Switched Systems Based on Fuzzy Theory and PSO," IOP Conference Series: Materials Science and Engineering, vol. 261, no. 1, pp. 012010, 2017. [Link]
  3. E. Atashpaz-Gargari, U. M. Braga-Neto and E. R. Dougherty, "Improved branch-and-bound algorithm for U-curve optimization," 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, 2013. [Link]
  4. E. Atashpaz-Gargari, R. Rajabioun, F. Hashemzadeh and F. Salmasi, "A decentralized PID controller based on optimal shrinkage of Gershgorin bands and PID tuning using colonial competitive algorithm," International Journal of Innovative Computing, Information and Control, vol. 5, no. 10, pp. 3227-3240, 2009.
  5. E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition," 2007 IEEE Congress on Evolutionary Computation, 2007. [Link]

Mikhail Belkin

  1. N. Mallinar, D. Beaglehole, L. Zhu, A. Radhakrishnan, P. Pandit and M. Belkin, "Emergence in non-neural models: grokking modular arithmetic via average gradient outer product," preprint (arXiv:2407.20199), 2024.
  2. D. Gedon, A. Abedsoltan, T. B. Schön and M. Belkin, "Uncertainty Estimation with Recursive Feature Machines," The 40th Conference on Uncertainty in Artificial Intelligence, 2024. [Link]
  3. D. Beaglehole, P. Súkeník, M. Mondelli and M. Belkin, "Average gradient outer product as a mechanism for deep neural collapse," preprint (arXiv:2402.13728), 2024.
  4. A. Radhakrishnan, D. Beaglehole, P. Pandit and M. Belkin, "Mechanism for feature learning in neural networks and backpropagation-free machine learning models," Science, 2024. [Link]
  5. A. Radhakrishnan, M. Belkin and D. Drusvyatskiy, "Linear Recursive Feature Machines provably recover low-rank matrices," preprint (arXiv:2401.04553), 2024.
  6. M. Belkin, "The necessity of machine learning theory in mitigating AI risk.," ACM/JMS Journal of Data Science, 2024. [Link]
  7. N. Ghosh and M. Belkin, "A universal trade-off between the model size, test loss, and training loss of linear predictors," SIAM Journal on Mathematics of Data Science, vol. 5, no. 4, pp. 977-1004, 2023. [Link]
  8. L. Zhu, C. Liu, A. Radhakrishnan and M. Belkin, "Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning," preprint (arXiv:2306.04815), 2023.
  9. L. Hui, M. Belkin and S. Wright, "Cut your losses with squentropy," International Conference on Machine Learning, 2023. [Link]
  10. J. B. Simon, D. Karkada, N. Ghosh and M. Belkin, "More is better in modern machine learning: when infinite overparameterization is optimal and overfitting is obligatory," preprint (arXiv:2311.14646), 2023.
  11. D. Beaglehole, M. Belkin and P. Pandit, "On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions," SIAM Journal on Mathematics of Data Science, vol. 5, no. 4, pp. 854-872, 2023. [Link]
  12. C. Liu, A. Abedsoltan and M. Belkin, "On Emergence of Clean-Priority Learning in Early Stopped Neural Networks," preprint (arXiv:2306.02533), 2023.
  13. A. Radhakrishnan, M. Belkin and C. Uhler, "Wide and deep neural networks achieve consistency for classification," Proceedings of the National Academy of Sciences, vol. 120, no. 14, 2023. [Link]
  14. A. Abedsoltan, M. Belkin, P. Pandit and L. Rademacher, "On the Nystrom Approximation for Preconditioning in Kernel Machines," preprint (arXiv:2312.03311), 2023.
  15. Y. Cao, Z. Chen, M. Belkin and Q. Gu, "Benign overfitting in two-layer convolutional neural networks," Advances in Neural Information Processing Systems, 2022. [Link]
  16. N. Mallinar, J. Simon, A. Abedsoltan, P. Pandit, M. Belkin and P. Nakkiran, "Benign, tempered, or catastrophic: Toward a refined taxonomy of overfitting," Advances in Neural Information Processing Systems, 2022. [Link]
  17. C. Liu, L. Zhu and M. Belkin, "Loss landscapes and optimization in over-parameterized non-linear systems and neural networks," Applied and Computational Harmonic Analysis, 2022. [Link]
  18. A. Radhakrishnan, G. Stefanakis, M. Belkin and C. Uhler, "Simple, fast, and flexible framework for matrix completion with infinite width neural networks," Proceedings of the National Academy of Sciences, vol. 119, no. 16, 2022. [Link]
  19. M. Belkin, "Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, 2021. [Link]
  20. C. Liu, L. Zhu and M. Belkin, "Toward a theory of optimization for over-parameterized systems of non-linear equations: The lessons of deep learning," preprint (arXiv:2003.00307), vol. 7, 2020.
  21. C. Liu, L. Zhu and M. Belkin, "On the linearity of large non-linear models: When and why the tangent kernel is constant," Advances in Neural Information Processing Systems, 2020. [Link]
  22. M. Belkin, D. Hsu, S. Ma and S. Mandal, "Reconciling modern machine-learning practice and the classical bias–variance trade-off," Proceedings of the National Academy of Sciences, vol. 116, no. 32, pp. 15849-15854, 2019. [Link]
  23. J. Eldridge, M. Belkin and Y. Wang, "Unperturbed: Spectral analysis beyond Davis-Kahan," Algorithmic Learning Theory, 2018. [Link]
  24. J. Eldridge, M. Belkin and Y. Wang, "Graphons, mergeons, and so on!" Advances in Neural Information Processing Systems, vol. 29, 2016. [Link]

Shirin Saeedi Bidokhti

  1. X. Chen, N. NaderiAlizadeh, A. Ribeiro and S. S. Bidokhti, "Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks," preprint (arXiv:2404.03227), 2024.
  2. X. Chen, H. Nikpey, J. Kim, S. Sarkar and S. Saeedi-Bidokhti, "Containing a spread through sequential learning: to exploit or to explore?" preprint (arXiv:2303.00141), 2023.
  3. E. Lei, Y. B. Uslu, H. Hassani and S. S. Bidokhti, "Text + Sketch: Image Compression at Ultra Low Rates," preprint (arXiv:2307.01944), 2023.
  4. E. Lei, H. Hassani and S. S. Bidokhti, "On a Relation Between the Rate-Distortion Function and Optimal Transport," preprint (arXiv:2307.00246), 2023. (Tiny Papers at ICML 2023.)
  5. E. Lei, H. Hassani and S. S. Bidokhti, "On a Relation Between the Rate-Distortion Function and Optimal Transport," International Conference on Learning Representations, 2023. (Collaboration with Hamed Hassani, Foundations team.) [Link]
  6. E. Lei, H. Hassani and S. S. Bidokhti, "Neural estimation of the rate-distortion function with applications to operational source coding," IEEE Journal on Selected Areas in Information Theory, 2023. [Link]
  7. E. Lei, H. Hassani and S. S. Bidokhti, "Federated neural compression under heterogeneous data," 2023 IEEE International Symposium on Information Theory (ISIT), 2023. (Collaboration with Hamed Hassani, Foundations team.) [Link]
  8. R. Arghal, E. Lei and S. S. Bidokhti, "Robust graph neural networks via probabilistic lipschitz constraints," Learning for Dynamics and Control Conference, 2022. [Link]
  9. X. Chen, X. Liao and S. S. Bidokhti, "Real-time sampling and estimation on random access channels: Age of information and beyond," IEEE INFOCOM 2021-IEEE Conference on Computer Communications, 2021. [Link]
  10. S. S. Bidokhti, M. Wigger and R. Timo, "Noisy broadcast networks with receiver caching," IEEE Transactions on Information Theory, vol. 64, no. 11, pp. 6996-7016, 2018. [Link]
  11. R. Timo, S. S. Bidokhti, M. Wigger and B. C. Geiger, "A rate-distortion approach to caching," IEEE Transactions on Information Theory, vol. 64, no. 3, pp. 1957-1976, 2017. [Link]
  12. S. S. Bidokhti and G. Kramer, "Capacity bounds for diamond networks with an orthogonal broadcast channel," IEEE Transactions on Information Theory, vol. 62, no. 12, pp. 7103-7122, 2016. [Link]

Henrik I. Christensen

  1. A. Almuzairee, N. Hansen and H. I. Christensen, "A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning," preprint (arXiv:2405.17416), 2024.
  2. H. I. Christensen, J. Biswas, M. Buehler, T. Danko, M. Gini, P. Khargonekar, M. Mataric, A. Okamura, N. Papanikolopoulos, B. Smart, M. Tolley, H. Yanco and W. Zhang, "US National Robotics Roadmap 2024," Computing Community Consortium (CCC) and Engineering Visioning Research Alliance (ERVA), 2024. [Link]
  3. Y. Qiu and H. I. Christensen, "3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs," 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023. [Link]
  4. S. R. Iyer, A. Pal, J. Hu, A. Adeleye, A. Aggarwal and H. I. Christensen, "Household navigation and manipulation for everyday object rearrangement tasks," preprint (arXiv:2312.06129), 2023. (Accepted at IEEE IRC-2023.)
  5. R. Patil, A. Langley and H. Christensen, "Scaling up multi-agent patrolling in urban environments," Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation, 2023. [Link]
  6. Q. Vuong, S. Levine, H. R. Walke, K. Pertsch, A. Singh, R. Doshi, C. Xu, J. Luo, L. Tan, H. Christensen, D. Shah, et al., "Open x-embodiment: Robotic learning datasets and RT-x models," Conference on Robot Learning (CoRL) 2023, 2023. [Link]
  7. J. Hu, Z. Tang and H. I. Christensen, "Multi-Modal Planning on Regrasping for Stable Manipulation," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023. [Link]
  8. P. Parashar, A. K. Goel, B. Sheneman and H. I. Christensen, "Towards life-long adaptive agents: Using metareasoning for combining knowledge-based planning with situated learning," The Knowledge Engineering Review, 2018. [Link]
  9. H. I. Christensen, A. Khan, S. Pokutta and P. Tetali, "Approximation and online algorithms for multidimensional bin packing: A survey," Computer Science Review, 2017. [Link]
  10. T. Kunz, A. Thomaz and H. Christensen, "Hierarchical rejection sampling for informed kinodynamic planning in high-dimensional spaces," 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016. [Link]
  11. M. Dogar, R. A. Knepper, A. Spielberg, C. Choi, H. I. Christensen and D. Rus, "Multi-scale assembly with robot teams," The International Journal of Robotics Research, vol. 34, no. 13, pp. 1645-1659, 2015. [Link]
  12. J. Folkesson and H. I. Christensen, "Graphical SLAM for outdoor applications," Journal of Field Robotics, vol. 24, no. 1-2, pp. 51-70, 2007. [Link]

Fan Chung Graham

  1. N. Sieger and F. Chung, "Quasi-Random Boolean Functions," The Electronic Journal of Combinatorics, 2024. [Link]
  2. F. Chung and N. Sieger, "Subgraph Counts in Random Clustering Graphs," Modelling and Mining Networks, 2024. [Link]
  3. F. C. Graham, "Regularity lemmas for clustering graphs," Advances in Applied Mathematics, 2021. [Link]
  4. F. C. Graham, R. Graham and S. Spiro, "Slow Fibonacci Walks," Journal of Number Theory, 2020. [Link]
  5. F. C. Graham and J. Tobin, "The spectral gap of graphs arising from substring reversals," The Electronic Journal of Combinatorics, 2017. [Link]
  6. S. Aksoy, F. C. Graham and X. Peng, "Extreme values of the stationary distribution of random walks on directed graphs," Advances in Applied Mathematics, 2016. [Link]
  7. F. C. Graham, "A Brief Survey of PageRank Algorithms," IEEE Trans. Netw. Sci. Eng., vol. 1, no. 1, pp. 38-42, 2014. [Link]

Sicun Gao

  1. Y. Zhai, Z. Qin and S. Gao, "Sample-and-Bound for Non-Convex Optimization," preprint (arXiv:2401.04812), 2024. (Published at AAAI 2024.)
  2. T. Wang, S. Herbert and S. Gao, "Mollification Effects of Policy Gradient Methods," preprint (arXiv:2405.17832), 2024.
  3. M. Ganai, S. Gao and S. Herbert, "Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey," preprint (arXiv:2407.09645), 2024.
  4. H. Yu and S. Gao, "Activation-Descent Regularization for Input Optimization of Re{LU} Networks," Forty-first International Conference on Machine Learning, 2024. [Link]
  5. C.-E. Sun, S. Gao and T.-W. Weng, "Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents," preprint (arXiv:2406.18062), 2024.
  6. T. Wang, S. Herbert and S. Gao, "Fractal Landscapes in Policy Optimization," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
  7. M. Ganai, Z. Gong, C. Yu, S. Herbert and S. Gao, "Iterative Reachability Estimation for Safe Reinforcement Learning," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
  8. M. Ganai, C. Hirayama, Y.-C. Chang and S. Gao, "Learning Stabilization Control from Observations by Learning Lyapunov-like Proxy Models," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Link]
  9. H. Yu, C. Hirayama, C. Yu, S. Herbert and S. Gao, "Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance," IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023. [Link]
  10. H. Shi, Y. Gu, Y. Zhou, B. Zhao, S. Gao and J. Zhao, "Everyone’s preference changes differently: A weighted multi-interest model for retrieval," International Conference on Machine Learning, 2023. [Link]
  11. C. Yu, Q. Li, S. Gao and A. Prorok, "Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Link]
  12. C. Yu, H. Yu and S. Gao, "Learning control admissibility models with graph neural networks for multi-agent navigation," Conference on Robot Learning, 2023. [Link]
  13. Y. Zhai and S. Gao, "Monte Carlo Tree Descent for Black-Box Optimization," Advances in Neural Information Processing Systems, 2022. [Link]
  14. R. Zhang, C. Yu, J. Chen, C. Fan and S. Gao, "Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding," Advances in Neural Information Processing Systems, 2022. [Link]
  15. E. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems," Advances in Neural Information Processing Systems, 2022. [Link]
  16. Y.-C. Chang, N. Roohi and S. Gao, "Neural lyapunov control," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  17. S. Kong, A. Solar-Lezama and S. Gao, "Delta-decision procedures for exists-forall problems over the reals," International Conference on Computer Aided Verification, 2018. [Link]
  18. S. Gao, S. Kong and E. M. Clarke, "dReal: An SMT solver for nonlinear theories over the reals," Automated Deduction CADE-24: 24th International Conference on Automated Deduction, 2013. [Link]
  19. S. Gao, J. Avigad and E. M. Clarke, "Delta-decidability over the reals," 2012 27th Annual IEEE Symposium on Logic in Computer Science, 2012. [Link]
  20. S. Gao, J. Avigad and E. M. Clarke, "Delta-complete decision procedures for satisfiability over the reals," International Joint Conference on Automated Reasoning, 2012. [Link]

Hamed Hassani

  1. X. Huang, S. Li, E. Dobriban, O. Bastani, H. Hassani and D. Ding, "One-Shot Safety Alignment for Large Language Models via Optimal Dualization," preprint (arXiv:2405.19544), 2024.
  2. S. Kiyani, G. Pappas and H. Hassani, "Length Optimization in Conformal Prediction," preprint (arXiv:2406.18814), 2024.
  3. M. Sabbaghi, G. Pappas, H. Hassani and S. Goel, "Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks," preprint (arXiv:2406.01895), 2024.
  4. H. Chang, H. Hassani and R. Shokri, "Watermark Smoothing Attacks against Language Models," preprint (arXiv:2407.14206), 2024.
  5. B. Moniri, H. Hassani and E. Dobriban, "Evaluating the Performance of Large Language Models via Debates," preprint (arXiv:2406.11044), 2024.
  6. B. Moniri and H. Hassani, "Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models," preprint (arXiv:2405.18274), 2024.
  7. E. Lei, Y. B. Uslu, H. Hassani and S. S. Bidokhti, "Text + Sketch: Image Compression at Ultra Low Rates," preprint (arXiv:2307.01944), 2023.
  8. E. Lei, H. Hassani and S. S. Bidokhti, "On a Relation Between the Rate-Distortion Function and Optimal Transport," preprint (arXiv:2307.00246), 2023. (Tiny Papers at ICML 2023.)
  9. E. Lei, H. Hassani and S. S. Bidokhti, "On a Relation Between the Rate-Distortion Function and Optimal Transport," International Conference on Learning Representations, 2023. [Link]
  10. E. Lei, H. Hassani and S. S. Bidokhti, "Neural estimation of the rate-distortion function with applications to operational source coding," IEEE Journal on Selected Areas in Information Theory, 2023. [Link]
  11. E. Lei, H. Hassani and S. S. Bidokhti, "Federated neural compression under heterogeneous data," 2023 IEEE International Symposium on Information Theory (ISIT), 2023. [Link]
  12. D. Lee, B. Moniri, X. Huang, E. Dobriban and H. Hassani, "Demystifying disagreement-on-the-line in high dimensions," International Conference on Machine Learning, 2023. [Link]
  13. B. Moniri, D. Lee, H. Hassani and E. Dobriban, "A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks," preprint (arXiv:2310.07891), 2023.
  14. A. Mitra, G. J. Pappas and H. Hassani, "Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning," preprint (arXiv:2301.00944), 2023.
  15. Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Self-consistency of the Fokker Planck equation," Conference on Learning Theory, 2022. [Link]
  16. H. Hassani and A. Javanmard, "The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression," preprint (arXiv:2201.05149), 2022.
  17. A. Mokhtari, H. Hassani and A. Karbasi, "Stochastic conditional gradient methods: From convex minimization to submodular maximization," The Journal of Machine Learning Research, vol. 21, no. 1, pp. 4232-4280, 2020. [Link]
  18. A. Fazeli, H. Hassani, M. Mondelli and A. Vardy, "Binary linear codes with optimal scaling: Polar codes with large kernels," IEEE Transactions on Information Theory, vol. 67, no. 9, pp. 5693-5710, 2020. [Link]
  19. H. Hassani, A. Karbasi, A. Mokhtari and Z. Shen, "Stochastic conditional gradient++: (Non)convex minimization and continuous submodular maximization," SIAM Journal on Optimization, vol. 30, no. 4, pp. 3315-3344, 2020. [Link]
  20. H. Hassani, S. Kudekar, O. Ordentlich, Y. Polyanskiy and R. Urbanke, "Almost optimal scaling of Reed-Muller codes on BEC and BSC channels," 2018 IEEE International Symposium on Information Theory (ISIT), 2018. [Link]
  21. M. Mondelli, S. H. Hassani and R. L. Urbanke, "Unified scaling of polar codes: Error exponent, scaling exponent, moderate deviations, and error floors," IEEE Transactions on Information Theory, vol. 62, no. 12, pp. 6698-6712, 2016. [Link]
  22. M. Mondelli, S. H. Hassani, I. Sason and R. L. Urbanke, "Achieving Marton’s region for broadcast channels using polar codes," IEEE Transactions on Information Theory, vol. 61, no. 2, pp. 783-800, 2014. [Link]

Tara Javidi

  1. X. Zheng, T. Javidi and B. Touri, "A General Framework for Approximate and Delayed Gradient Descent for Decomposable Cost Functions," IEEE Control Systems Letters, 2024. [Link]
  2. M. Soleymani and T. Javidi, "A Non-Adaptive Algorithm for the Quantitative Group Testing Problem," The Thirty Seventh Annual Conference on Learning Theory, 2024. [Link]
  3. A. Ghosh, A. Sankararaman, K. Ramchandran, T. Javidi and A. Mazumdar, "Competing Bandits in Non-Stationary Matching Markets," IEEE Transactions on Information Theory, 2024. [Link]
  4. X. Zheng, T. Javidi and B. Touri, "Zeroth-Order Non-Convex Optimization for Cooperative Multi-Agent Systems With Diminishing Step Size and Smoothing Radius," IEEE Control Systems Letters, 2023. [Link]
  5. V. Kungurtsev, M. Morafah, T. Javidi and G. Scutari, "Decentralized asynchronous non-convex stochastic optimization on directed graphs," IEEE Transactions on Control of Network Systems, 2023. [Link]
  6. V. Kungurtsev, A. Cobb, T. Javidi and B. Jalaian, "Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo," Machine Learning, vol. 112, no. 8, pp. 2791-2819, 2023. [Link]
  7. M. Lee, O. S. Haddadin and T. Javidi, "FFT-Based Approximations for Black-Box Optimization," 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023. [Link]
  8. J. Elenter, N. NaderiAlizadeh, T. Javidi and A. Ribeiro, "Primal-Dual Continual Learning: Stability and Plasticity through Lagrange Multipliers," preprint (arXiv:2310.00154), 2023.
  9. X. Wang, N. Heydaribeni, F. Koushanfar and T. Javidi, "Federated Certainty Equivalence Control for Linear Gaussian Systems with Unknown Decoupled Dynamics and Quadratic Common Cost," 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2023. [Link]
  10. S. Shekhar and T. Javidi, "Instance Dependent Regret Analysis of Kernelized Bandits," International Conference on Machine Learning, 2022. [Link]
  11. M. Lee, S. Shekhar and T. Javidi, "Multi-scale zero-order optimization of smooth functions in an RKHS," 2022 IEEE International Symposium on Information Theory (ISIT), 2022. [Link]
  12. X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-peer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172-182, 2022. [Link]
  13. M. Javaheripi, M. Samragh, T. Javidi and F. Koushanfar, "AdaNS: Adaptive non-uniform sampling for automated design of compact DNNs," IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 4, pp. 750-764, 2020. [Link]
  14. M. J. Khojasteh, A. Khina, M. Franceschetti and T. Javidi, "Learning-based attacks in cyber-physical systems," IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 437-449, 2020. [Link]
  15. B. D. Rouani, M. Samragh, T. Javidi and F. Koushanfar, "Safe machine learning and defeating adversarial attacks," IEEE Security & Privacy, vol. 17, no. 2, pp. 31-38, 2019. [Link]
  16. A. Lalitha, N. Ronquillo and T. Javidi, "Improved target acquisition rates with feedback codes," IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 5, pp. 871-885, 2018. [Link]
  17. M. Rao, A. Kipnis, T. Javidi, Y. C. Eldar and A. Goldsmith, "System identification from partial samples: Non-asymptotic analysis," 2016 IEEE 55th Conference on Decision and Control (CDC), 2016. [Link]
  18. T. Javidi, Y. Kaspi and H. Tyagi, "Gaussian estimation under attack uncertainty," 2015 IEEE Information Theory Workshop (ITW), 2015. [Link]

Shatha Jawad

  1. R. P. Uhlig, S. Jawad, P. Zamora and E. Niven, "Ethical Use of Generative AI in Engineering: Assessing Students and Preventing them from Cheating Themselves," 2024 ASEE Annual Conference, 2024. [Link]
  2. R. P. Uhlig, S. Jawad, B. Sinha, P. P. Dey and M. N. Amin, "Student Use of Artificial Intelligence to Write Technical Engineering Papers—Cheating or a Tool to Augment Learning," 2023 ASEE Annual Conference & Exposition, 2023. [Link]
  3. S. Jawad, R. P. Uhlig, P. P. Dey, M. N. Amin and B. Sinha, "Using Artificial Intelligence in Academia to Help Students Choose Their Engineering Program," 2023 ASEE Annual Conference & Exposition, 2023. [Link]
  4. S. K. Jawad, R. P. Uhlig, B. Sinha, M. N. Amin and P. P. Dey, "Multithread Affinity Scheduling Using a Decision Maker," Asian Journal of Computer and Information Systems, vol. 6, no. 4, 2018. [Link]
  5. S. Jawad, "Design and evaluation of a neurofuzzy CPU scheduling algorithm," Proceedings of the 11th IEEE International Conference on Networking, Sensing, and Control, 2014. [Link]
  6. S. K. Jawad, R. Rzouq, S. Hiary, S. Issa and A. Garageer, "A Design of Facial Recognition System Using Neural Network Based Geometrics 3D Facial," Proceedings of the 6th IASTED International Conference, vol. 643, no. 63, 2009. [Link]

Stefanie Jegelka

  1. X. Wu, A. Ajorlou, Y. Wang, S. Jegelka and A. Jadbabaie, "On the Role of Attention Masks and LayerNorm in Transformers," preprint (arXiv:2405.18781), 2024.
  2. T. Le, L. Ruiz and S. Jegelka, "A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs," The Twelfth International Conference on Learning Representations, 2024. [Link]
  3. S. Gupta, J. Robinson, D. Lim, S. Villar and S. Jegelka, "Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning," 12th International Conference on Learning Representations, 2024. [Link]
  4. S. Gupta, C. Wang, Y. Wang, T. Jaakkola and S. Jegelka, "In-Context Symmetries: Self-Supervised Learning through Contextual World Models," ICML 2024 Workshop on In-Context Learning, 2024. [Link]
  5. K. Gatmiry, Z. Li, S. J. Reddi and S. Jegelka, "Simplicity Bias via Global Convergence of Sharpness Minimization," Forty-first International Conference on Machine Learning, 2024. [Link]
  6. K. Gatmiry, N. Saunshi, S. J. Reddi, S. Jegelka and S. Kumar, "Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?" Forty-first International Conference on Machine Learning, 2024. [Link]
  7. G. Ma, Y. Wang, D. Lim, S. Jegelka and Y. Wang, "A Canonization Perspective on Invariant and Equivariant Learning," preprint (arXiv:2405.18378), 2024.
  8. D. Lim, T. Putterman, R. Walters, H. Maron and S. Jegelka, "The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof," High-dimensional Learning Dynamics 2024: The Emergence of Structure and Reasoning, 2024. [Link]
  9. C. Morris, F. Frasca, N. Dym, H. Maron, I. I. Ceylan, R. Levie, D. Lim, M. M. Bronstein, M. Grohe and S. Jegelka, "Position: Future Directions in the Theory of Graph Machine Learning," Forty-first International Conference on Machine Learning, 2024. [Link]
  10. B. Tahmasebi, A. Soleymani, D. Bahri, S. Jegelka and P. Jaillet, "A Universal Class of Sharpness-Aware Minimization Algorithms," Forty-first International Conference on Machine Learning, 2024. [Link]
  11. B. Kiani, T. Le, H. Lawrence, S. Jegelka and M. Weber, "On the hardness of learning under symmetries," The Twelfth International Conference on Learning Representations, 2024. [Link]
  12. Y. Wang, Y. Wu, Z. Wei, S. Jegelka and Y. Wang, "A Theoretical Understanding of Self-Correction through In-context Alignment," ICML 2024 Workshop on In-Context Learning, 2024. [Link]
  13. Y. Wang, K. Hu, S. Gupta, Z. Ye, Y. Wang and S. Jegelka, "Understanding the Role of Equivariance in Self-supervised Learning," ICML 2024 Workshop on Theoretical Foundations of Foundation Models, 2024. [Link]
  14. S. Gupta, J. Robinson, D. Lim, S. Villar and S. Jegelka, "Learning Structured Representations with Equivariant Contrastive Learning," ICML 2nd Annual Topology, Algebra, and Geometry in Machine Learning Workshop, 2023. [Link]
  15. K. Gatmiry, Z. Li, T. Ma, S. J. Reddi, S. Jegelka and C.-Y. Chuang, "What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models," Thirty-seventh Conference on Neural Information Processing Systems, 2023. [Link]
  16. K. Gatmiry, Z. Li, C.-Y. Chuang, S. Reddi, T. Ma and S. Jegelka, "The Inductive Bias of Flatness Regularization for Deep Matrix Factorization," preprint (arXiv:2306.13239), 2023.
  17. D. Lim, J. Robinson, S. Jegelka and H. Maron, "Expressive Sign Equivariant Networks for Spectral Geometric Learning," Thirty-seventh Conference on Neural Information Processing Systems, 2023. [Link]
  18. C.-Y. Chuang, S. Jegelka and D. Alvarez-Melis, "InfoOT: Information maximizing optimal transport," International Conference on Machine Learning, 2023. [Link]
  19. B. Tahmasebi, A. Soleymani, S. Jegelka and P. Jaillet, "On Scale-Invariant Sharpness Measures," NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 2023. [Link]
  20. B. Tahmasebi and S. Jegelka, "Sample Complexity Bounds for Estimating the Wasserstein Distance under Invariances," ICML 2nd Annual Topology, Algebra, and Geometry in Machine Learning Workshop, 2023. [Link]
  21. N. Karalias, J. Robinson, A. Loukas and S. Jegelka, "Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions," Advances in Neural Information Processing Systems, 2022. [Link]
  22. N. Chandramoorthy, A. Loukas, K. Gatmiry and S. Jegelka, "On the generalization of learning algorithms that do not converge," Advances in Neural Information Processing Systems, 2022. [Link]
  23. D. Lim, J. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron and S. Jegelka, "Sign and basis invariant networks for spectral graph representation learning," preprint (arXiv:2202.13013), 2022. (Spotlight/notable top 25%)
  24. C.-Y. Chuang and S. Jegelka, "Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks," Advances in Neural Information Processing Systems, 2022. [Link]
  25. S. Jegelka, "Theory of graph neural networks: Representation and learning," The International Congress of Mathematicians, 2022. [Link]
  26. K. Gatmiry, S. Jegelka and J. Kelner, "Optimization and Adaptive Generalization of Three-layer Neural Networks," International Conference on Learning Representations, 2021. [Link]
  27. J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka and S. Sra, "Can contrastive learning avoid shortcut solutions?" Advances in neural information processing systems, 2021. [Link]
  28. V. Garg, S. Jegelka and T. Jaakkola, "Generalization and representational limits of graph neural networks," International Conference on Machine Learning, 2020. [Link]
  29. K. Xu, J. Li, M. Zhang, S. S. Du, K.-i. Kawarabayashi and S. Jegelka, "What can neural networks reason about?" preprint (arXiv:1905.13211), 2019.
  30. K. Xu, W. Hu, J. Leskovec and S. Jegelka, "How powerful are graph neural networks?" preprint (arXiv:1810.00826), 2018.
  31. M. Staib and S. Jegelka, "Robust budget allocation via continuous submodular functions," International Conference on Machine Learning, 2017. [Link]
  32. R. Iyer, S. Jegelka and J. Bilmes, "Fast semidifferential-based submodular function optimization," International Conference on Machine Learning, 2013. [Link]

Andrew B. Kahng

  1. S. Choi, J. Jung, A. B. Kahng, M. Kim, C.-H. Park, B. Pramanik and D. Yoon, "PROBE3.0: A Systematic Framework for Design-Technology Pathfinding With Improved Design Enablement," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 43, no. 4, pp. 1218-1231, 2024. [Link]
  2. A. B. Kahng, S. Kundu and S. Thumathy, "Scalable Flip-Flop Clustering Using Divide and Conquer For Capacitated K-Means," Proceedings of the Great Lakes Symposium on VLSI 2024, 2024. [Link]
  3. A. B. Kahng, R. R. Nerem, Y. Wang and C.-Y. Yang, "NN-Steiner: A mixed neural-algorithmic approach for the rectilinear Steiner minimum tree problem," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 12, pp. 13022-13030, 2024. [Link]
  4. A. B. Kahng, B. Pramanik and M. Woo, "A Hybrid ECO Detailed Placement Flow for Improved Reduction of Dynamic IR Drop," Proceedings of the Great Lakes Symposium on VLSI 2024, 2024. [Link]
  5. A. B. Kahng, A. Mazumdar, J. Reeves and Y. Wang, "The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics," AI Magazine, 2024. [Link]
  6. V. A. Chhabria, W. Jiang, A. B. Kahng and S. S. Sapatnekar, "A Machine Learning Approach to Improving Timing Consistency between Global Route and Detailed Route," Association for Computing Machinery, 2023. [Link]
  7. J. Jung, A. B. Kahng, S. Kundu, Z. Wang and D. Yoon, "Invited Paper: IEEE CEDA DATC Emerging Foundations in IC Physical Design and MLCAD Research," 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023. (Collaboration with Jinwook Jung of IBM.) [Link]
  8. J. Hu and A. B. Kahng, "The Inevitability of AI Infusion Into Design Closure and Signoff," 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023. [Link]
  9. I. Bustany, G. Gasparyan, A. B. Kahng, I. Koutis, B. Pramanik and Z. Wang, "An Open-Source Constraints-Driven General Partitioning Multi-Tool for VLSI Physical Design," 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023. (Collaboration with Ismail Bustany and Grigor Gasparyan of AMD.) [Link]
  10. C.-K. Cheng, A. B. Kahng, S. Kundu, Y. Wang and Z. Wang, "Assessment of Reinforcement Learning for Macro Placement," Proceedings of the 2023 International Symposium on Physical Design, 2023. (Invited paper.) [Link]
  11. C.-K. Cheng, A. B. Kahng, B. Lin, Y. Wang and D. Yoon, "Gear-Ratio-Aware Standard Cell Layout Framework for DTCO Exploration," Association for Computing Machinery, 2023. [Link]
  12. A. B. Kahng, S. Thumathy and M. Woo, "An Effective Cost-Skew Tradeoff Heuristic for VLSI Global Routing," 24th International Symposium on Quality Electronic Design, 2023. [Link]
  13. V. A. Chhabria, W. Jiang, A. B. Kahng and S. S. Sapatnekar, "From Global Route to Detailed Route: ML for Fast and Accurate Wire Parasitics and Timing Prediction," Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022. [Link]
  14. I. Bustany, A. B. Kahng, I. Koutis, B. Pramanik and Z. Wang, "SpecPart: A supervised spectral framework for hypergraph partitioning solution improvement," Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022. (Best paper award.) [Link]
  15. H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. Sapatnekar, Z. Wang and Z. Zeng, "An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators," ACM/IEEE International Symposium on Machine Learning for CAD, 2022. [Link]
  16. H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. S. Sapatnekar, Z. Wang, et al., "Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms," Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022. [Link]
  17. A. B. Kahng, R. Varadarajan and Z. Wang, "RTL-MP: Toward practical, human-quality chip planning and macro placement," Proceedings of the 2022 International Symposium on Physical Design, 2022. [Link]
  18. A. B. Kahng, "Machine Learning for CAD/EDA: The Road Ahead," IEEE Design and Test, vol. 40, no. 1, pp. 8-16, 2022. (Special issue on machine learning for CAD/EDA.) [Link]
  19. A. B. Kahng, "Leveling up: A trajectory of OpenROAD, TILOS, and beyond," Proceedings of the 2022 International Symposium on Physical Design, 2022. [Link]
  20. A. B. Kahng, "A Mixed Open-Source and Proprietary EDA Commons for Education and Prototyping," Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022. (Invited paper.) [Link]
  21. A. B. Kahng and Z. Wang, "ML for Design QoR Prediction," Machine Learning Applications in Electronic Design Automation, 2022. [Link]
  22. J. Jung, A. B. Kahng, S. Kim and R. Varadarajan, "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD ICCAD Special Session Paper," IEEE/ACM International Conference On Computer Aided Design, 2021. (Invited paper.) [Link]
  23. J. Chen, I. H.-R. Jiang, J. Jung, A. B. Kahng, S. Kim, V. N. Kravets, Y.-L. Li, R. Varadarajan and M. Woo, "DATC RDF-2021: Design flow and beyond iccad special session paper," IEEE/ACM International Conference On Computer Aided Design, 2021. (Invited paper.) [Link]
  24. C.-K. Cheng, A. B. Kahng, I. Kang, M. Kim, D. Lee, B. Lin, D. Park and M. Woo, "Core-eco: Concurrent refinement of detailed place-and-route for an efficient eco automation," IEEE 39th International Conference on Computer Design, 2021. [Link]
  25. A. B. Kahng, "Machine learning applications in physical design: Recent results and directions," Proceedings of the 2018 International Symposium on Physical Design, 2018. [Link]
  26. W.-T. J. Chan, P.-H. Ho, A. B. Kahng and P. Saxena, "Routability optimization for industrial designs at sub-14nm process nodes using machine learning," Proceedings of the 2017 ACM on International Symposium on Physical Design, 2017. [Link]
  27. C. J. Alpert, T. F. Chan, D. J.-H. Huang, A. B. Kahng, I. L. Markov, P. Mulet and K. Yan, "Faster minimization of linear wirelength for global placement," Proceedings of the 1997 International Symposium on Physical Design, 1997. [Link]
  28. K. D. Boese, A. B. Kahng and S. Muddu, "A new adaptive multi-start technique for combinatorial global optimizations," Operations Research Letters, vol. 16, no. 2, pp. 101-113, 1994. [Link]
  29. L. Hagen and A. B. Kahng, "New spectral methods for ratio cut partitioning and clustering," IEEE Transactions on CAD of Integrated Circuits and Systems, vol. 11, no. 9, pp. 1074-1085, 1992. [Link]

Amin Karbasi

  1. P. Okanovic, R. Waleffe, V. Mageirakos, K. E. Nikolakakis, A. Karbasi, D. Kalogerias, N. M. Gürel and T. Rekatsinas, "Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning," preprint (arXiv:2305.18424), 2024. (To appear at ICLR 2024.)
  2. L. R. Mualem, E. R. Elenberg, M. Feldman and A. Karbasi, "Submodular minimax optimization: Finding effective sets," International Conference on Artificial Intelligence and Statistics, 2024. [Link]
  3. I. Han, R. Jayaram, A. Karbasi, V. Mirrokni, D. Woodruff and A. Zandieh, "HyperAttention: Long-context Attention in Near-Linear Time," The Twelfth International Conference on Learning Representations, 2024. [Link]
  4. I. Attias, S. Hanneke, A. Kalavasis, A. Karbasi and G. Velegkas, "Universal Rates for Regression: Separations between Cut-Off and Absolute Loss," The Thirty Seventh Annual Conference on Learning Theory, 2024. [Link]
  5. B. Saglam, Z. Yang, D. Kalogerias and A. Karbasi, "Learning Task Representations from In-Context Learning," ICML 2024 Workshop on In-Context Learning, 2024. [Link]
  6. A. Kalavasis, A. Karbasi, G. Velegkas and F. Zhou, "On the Computational Landscape of Replicable Learning," preprint (arXiv:2405.15599), 2024.
  7. A. Kalavasis, A. Karbasi, A. Oikonomou, K. Sotiraki, G. Velegkas and M. Zampetakis, "Injecting Undetectable Backdoors in Deep Learning and Language Models," preprint (arXiv:2406.05660), 2024.
  8. A. Karbasi and K. G. Larsen, "The impossibility of parallelizing boosting," International Conference on Algorithmic Learning Theory, 2024. [Link]
  9. L. Zhang, J. Yang, A. Karbasi and N. He, "Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
  10. I. Attias, S. Hanneke, A. Kalavasis, A. Karbasi and G. Velegkas, "Optimal learners for realizable regression: Pac learning and online learning," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
  11. H. Esfandiari, A. Karbasi, V. Mirrokni, G. Velegkas and F. Zhou, "Replicable clustering," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
  12. A. Zandieh, I. Han, M. Daliri and A. Karbasi, "KDEformer: Accelerating transformers via kernel density estimation," International Conference on Machine Learning, 2023. [Link]
  13. A. Kalavasis, A. Karbasi, S. Moran and G. Velegkas, "Statistical indistinguishability of learning algorithms," International Conference on Machine Learning, 2023. [Link]
  14. A. Karbasi, N. L. Kuang, Y. Ma and S. Mitra, "Langevin Thompson sampling with logarithmic communication: bandits and reinforcement learning," International Conference on Machine Learning, 2023. [Link]
  15. A. Karbasi, G. Velegkas, L. Yang and F. Zhou, "Replicability in reinforcement learning," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
  16. Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Self-consistency of the Fokker Planck equation," Conference on Learning Theory, 2022. [Link]
  17. W. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular maximization in clean linear time," Advances in Neural Information Processing Systems, 2022. [Link]
  18. S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal rates for interactive learning," Advances in Neural Information Processing Systems, 2022. [Link]
  19. S. Hanneke, A. Karbasi, M. Mahmoody, I. Mehalel and S. Moran, "On optimal learning under targeted data poisoning," Advances in Neural Information Processing Systems, 2022. [Link]
  20. K. E. Nikolakakis, F. Haddadpour, A. Karbasi and D. S. Kalogerias, "Beyond lipschitz: Sharp generalization and excess risk bounds for full-batch gd," preprint (arXiv:2204.12446), 2022.
  21. J. Dadashkarimi, A. Karbasi and D. Scheinost, "Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport," International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022. [Link]
  22. I. Han, M. Gartrell, E. Dohmatob and A. Karbasi, "Scalable MCMC sampling for nonsymmetric determinantal point processes," International Conference on Machine Learning, 2022. [Link]
  23. I. Han, A. Zandieh, J. Lee, R. Novak, L. Xiao and A. Karbasi, "Fast neural kernel embeddings for general activations," Advances in Neural Information Processing Systems, 2022. [Link]
  24. G. Velegkas, Z. Yang and A. Karbasi, "The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches," preprint (arXiv:2203.01491), 2022.
  25. A. Kalavasis, G. Velegkas and A. Karbasi, "Multiclass learnability beyond the pac framework: Universal rates and partial concept classes," Advances in Neural Information Processing Systems, 2022. [Link]
  26. L. Chen, Q. Yu, H. Lawrence and A. Karbasi, "Minimax regret of switching-constrained online convex optimization: No phase transition," Advances in Neural Information Processing Systems, 2020. [Link]
  27. E. Tohidi, R. Amiri, M. Coutino, D. Gesbert, G. Leus and A. Karbasi, "Submodularity in action: From machine learning to signal processing applications," IEEE Signal Processing Magazine, vol. 37, no. 5, pp. 120-133, 2020. [Link]
  28. A. Mokhtari, H. Hassani and A. Karbasi, "Stochastic conditional gradient methods: From convex minimization to submodular maximization," The Journal of Machine Learning Research, vol. 21, no. 1, pp. 4232-4280, 2020. [Link]
  29. H. Hassani, A. Karbasi, A. Mokhtari and Z. Shen, "Stochastic conditional gradient++: (Non)convex minimization and continuous submodular maximization," SIAM Journal on Optimization, vol. 30, no. 4, pp. 3315-3344, 2020. [Link]
  30. B. Mirzasoleiman, A. Karbasi, R. Sarkar and A. Krause, "Distributed submodular maximization," The Journal of Machine Learning Research, vol. 17, no. 1, pp. 8330-8373, 2016. [Link]

Farinaz Koushanfar

  1. R. Zhang, R. S. Rajarathnam, D. Z. Pan and F. Koushanfar, "ICMarks: A Robust Watermarking Framework for Integrated Circuit Physical Design IP Protection," preprint (arXiv:2404.18407), 2024.
  2. N. Sheybani and F. Koushanfar, "You Can Have Your Cake and Eat It Too: Ensuring Practical Robustness and Privacy in Federated Learning," Proceedings of the AAAI Symposium Series, vol. 3, no. 1, pp. 316-316, 2024. [Link]
  3. N. Heydaribeni, X. Zhan, R. Zhang, T. Eliassi-Rad and F. Koushanfar, "Distributed constrained combinatorial optimization leveraging hypergraph neural networks," Nature Machine Intelligence, 2024. [Link]
  4. S. Hussain, T. Huster, C. Mesterharm, P. Neekhara and F. Koushanfar, "ReFace: Adversarial Transformation Networks for Real-time Attacks on Face Recognition Systems," 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2023. (Collaboration with Todd Huster of Peraton Labs.) [Link]
  5. R. Zhang, S. Hussain, H. Chen, M. Javaheripi and F. Koushanfar, "Systemization of Knowledge: Robust Deep Learning Using Hardware-Software Co-Design in Centralized and Federated Settings," ACM Trans. Des. Autom. Electron. Syst., vol. 28, no. 6, 2023. [Link]
  6. R. Zhang, M. Javaheripi, Z. Ghodsi, A. Bleiweiss and F. Koushanfar, "AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs," 60th ACM/IEEE Design Automation Conference, 2023. (Collaboration with Amit Bleiweiss of Intel.) [Link]
  7. K. Babel, M. Javaheripi, Y. Ji, M. Kelkar, F. Koushanfar and A. Juels, "Lanturn: Measuring Economic Security of Smart Contracts Through Adaptive Learning," Association for Computing Machinery, 2023. [Link]
  8. H. Chen, X. Zhang, K. Huang and F. Koushanfar, "AdaTest: Reinforcement learning and adaptive sampling for on-chip hardware Trojan detection," ACM Transactions on Embedded Computing Systems, vol. 22, no. 2, pp. 1-23, 2023. [Link]
  9. H. Chen, C. Fu, B. D. Rouhani, J. Zhao and F. Koushanfar, "Intellectual Property Protection of Deep Learning Systems via Hardware/Software Co-design," IEEE Design & Test, 2023. (Collaboration with Bita Darvish Rouhani of Microsoft.) [Link]
  10. X. Wang, N. Heydaribeni, F. Koushanfar and T. Javidi, "Federated Certainty Equivalence Control for Linear Gaussian Systems with Unknown Decoupled Dynamics and Quadratic Common Cost," 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2023. [Link]
  11. X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-peer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172-182, 2022. [Link]
  12. M. Javaheripi, M. Samragh, T. Javidi and F. Koushanfar, "AdaNS: Adaptive non-uniform sampling for automated design of compact DNNs," IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 4, pp. 750-764, 2020. [Link]
  13. P. Neekhara, S. Hussain, P. Pandey, S. Dubnov, J. McAuley and F. Koushanfar, "Universal adversarial perturbations for speech recognition systems," preprint (arXiv:1905.03828), 2019.
  14. H. Chen, C. Fu, B. D. Rouhani, J. Zhao and F. Koushanfar, "DeepAttest: An end-to-end attestation framework for deep neural networks," Proceedings of the 46th International Symposium on Computer Architecture, 2019. [Link]
  15. B. D. Rouani, M. Samragh, T. Javidi and F. Koushanfar, "Safe machine learning and defeating adversarial attacks," IEEE Security & Privacy, vol. 17, no. 2, pp. 31-38, 2019. [Link]
  16. A. Mirhoseini, E. L. Dyer, E. M. Songhori, R. Baraniuk and F. Koushanfar, "RankMap: A framework for distributed learning from dense data sets," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 2717-2730, 2017. [Link]
  17. B. D. Rouhani, A. Mirhoseini, E. M. Songhori and F. Koushanfar, "Automated real-time analysis of streaming big and dense data on reconfigurable platforms," ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 10, no. 1, pp. 1-22, 2016. [Link]

Vijay Kumar

  1. Y. Wu, Y. Tao, I. Spasojevic and V. Kumar, "Trajectory Optimization with Global Yaw Parameterization for Field-of-View Constrained Autonomous Flight," preprint (arXiv:2403.17067), 2024. (Submitted to IEEE International Conference on Intelligent Robots and Systems 2024.)
  2. Y. Wu, X. Sun, I. Spasojevic and V. Kumar, "Deep Learning for Optimization of Trajectories for Quadrotors," IEEE Robotics and Automation Letters, vol. 9, no. 3, pp. 2479-2486, 2024. [Link]
  3. Y. Tao, X. Liu, I. Spasojevic, S. Agarwal and V. Kumar, "3D Active Metric-Semantic SLAM," IEEE Robotics and Automation Letters, 2024. [Link]
  4. Y. S. Shao, Y. Wu, L. Jarin-Lipschitz, P. Chaudhari and V. Kumar, "Design and Evaluation of Motion Planners for Quadrotors in Environments with Varying Complexities," preprint (arXiv:2309.13720), 2024. (To appear at IEEE ICRA 2024.)
  5. X. Liu, J. Lei, A. Prabhu, Y. Tao, I. Spasojevic, P. Chaudhari, N. Atanasov and V. Kumar, "SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation," preprint (arXiv:2406.17249), 2024.
  6. K. Mao, I. Spasojevic, M. A. Hsieh and V. Kumar, "TOPPQuad: Dynamically-Feasible Time Optimal Path Parametrization for Quadrotors," preprint (arXiv:2309.11637), 2024.
  7. I. D. Miller, F. Cladera, T. Smith, C. J. Taylor and V. Kumar, "Air-Ground Collaboration with SPOMP: Semantic Panoramic Online Mapping and Planning," IEEE Transactions on Field Robotics, 2024. [Link]
  8. H. Zhang, A. Srikanthan, S. Folk, V. Kumar and N. Matni, "Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems," preprint (arXiv:2401.04960), 2024. (Submitted to Learning for Dynamics & Control (L4DC) 2024.)
  9. F. Cladera, I. D. Miller, Z. Ravichandran, V. Murali, J. Hughes, M. A. Hsieh, C. J. Taylor and V. Kumar, "Challenges and Opportunities for Large-Scale Exploration with Air-Ground Teams using Semantics," preprint (arXiv:2405.07169), 2024.
  10. A. Prabhu, X. Liu, I. Spasojevic, Y. Wu, Y. Shao, D. Ong, J. Lei, P. C. Green, P. Chaudhari and V. Kumar, "UAVs for forestry: Metric-semantic mapping and diameter estimation with autonomous aerial robots," Mechanical Systems and Signal Processing, 2024. [Link]
  11. Y. Tao, Y. Wu, B. Li, F. Cladera, A. Zhou, D. Thakur and V. Kumar, "SEER: Safe efficient exploration for aerial robots using learning to predict information gain," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Link]
  12. Y. Tao, E. Iceland, B. Li, E. Zwecher, U. Heinemann, A. Cohen, A. Avni, O. Gal, A. Barel and V. Kumar, "Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles," preprint (arXiv:2309.06986), 2023. (Submitted to IEEE ICRA 2024.)
  13. X. Liu, A. Prabhu, F. Cladera, I. D. Miller, L. Zhou, C. J. Taylor and V. Kumar, "Active metric-semantic mapping by multiple aerial robots," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Link]
  14. L. Zhou and V. Kumar, "Robust multi-robot active target tracking against sensing and communication attacks," IEEE Transactions on Robotics, 2023. [Link]
  15. K. Mao, J. Welde, M. A. Hsieh and V. Kumar, "Trajectory Planning for the Bidirectional Quadrotor as a Differentially Flat Hybrid System," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Link]
  16. J. Welde, M. D. Kvalheim and V. Kumar, "A Compositional Approach to Certifying Almost Global Asymptotic Stability of Cascade Systems," IEEE Control Systems Letters, 2023. [Link]
  17. I. Spasojevic, X. Liu, A. Prabhu, A. Ribeiro, G. J. Pappas and V. Kumar, "Robust localization of aerial vehicles via active control of identical ground vehicles," International Conference on Intelligent Robots and Systems, 2023. [Link]
  18. I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," Robotics: Science and Systems, 2023. [Link]
  19. I. Boero, I. Spasojevic, M. Del Castillo, G. Pappas, V. Kumar and A. Ribeiro, "Navigation with shadow prices to optimize multi-commodity flow rates," 62nd IEEE Conference on Decision and Control (CDC), 2023. [Link]
  20. F. Cladera, Z. Ravichandran, I. D. Miller, M. A. Hsieh, C. J. Taylor and V. Kumar, "Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications," preprint (arXiv:2309.15975), 2023.
  21. D. Cheng, F. C. Ojeda, A. Prabhu, X. Liu, A. Zhu, P. C. Green, R. Ehsani, P. Chaudhari and V. Kumar, "TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards," preprint (arXiv:2310.02162), 2023. (Submitted to IEEE ICRA 2024.)
  22. A. Srikanthan, F. Yang, I. Spasojevic, D. Thakur, V. Kumar and N. Matni, "A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023. [Link]
  23. X. Liu, S. W. Chen, G. V. Nardari, C. Qu, F. C. Ojeda, C. J. Taylor and V. Kumar, "Challenges and opportunities for autonomous micro-UAVs in precision agriculture," IEEE Micro, vol. 42, no. 1, pp. 61-68, 2022. [Link]
  24. S. W. Chen, T. Wang, N. Atanasov, V. Kumar and M. Morari, "Large scale model predictive control with neural networks and primal active sets," Automatica, 2022. [Link]
  25. S. Mayya, R. K. Ramachandran, L. Zhou, V. Senthil, D. Thakur, G. S. Sukhatme and V. Kumar, "Adaptive and risk-aware target tracking for robot teams with heterogeneous sensors," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5615-5622, 2022. [Link]
  26. N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar and A. Rajeswaran, "MoDem: Accelerating visual model-based reinforcement learning with demonstrations," preprint (arXiv:2212.05698), 2022.
  27. L. Jarin-Lipschitz, X. Liu, Y. Tao and V. Kumar, "Experiments in adaptive replanning for fast autonomous flight in forests," 2022 International Conference on Robotics and Automation (ICRA), 2022. [Link]
  28. K. Sun, S. Chaves, P. Martin and V. Kumar, "RTGNN: A novel approach to model stochastic traffic dynamics," 2022 International Conference on Robotics and Automation (ICRA), 2022. [Link]
  29. D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5552-5559, 2022. [Link]
  30. T. Nguyen, K. Mohta, C. J. Taylor and V. Kumar, "Vision-based multi-MAV localization with anonymous relative measurements using coupled probabilistic data association filter," 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. [Link]
  31. E. Tolstaya, F. Gama, J. Paulos, G. Pappas, V. Kumar and A. Ribeiro, "Learning decentralized controllers for robot swarms with graph neural networks," Conference on Robot Learning, 2020. [Link]
  32. M. Quigley, K. Mohta, S. S. Shivakumar, M. Watterson, Y. Mulgaonkar, M. Arguedas, K. Sun, S. Liu, B. Pfrommer, V. Kumar, et al., "The open vision computer: An integrated sensing and compute system for mobile robots," 2019 International Conference on Robotics and Automation (ICRA), 2019. [Link]
  33. J. Paulos, S. W. Chen, D. Shishika and V. Kumar, "Decentralization of multiagent policies by learning what to communicate," 2019 International Conference on Robotics and Automation (ICRA), 2019. [Link]
  34. X. Liu, S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das and V. Kumar, "Robust fruit counting: Combining deep learning, tracking, and structure from motion," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [Link]
  35. K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y. Mulgaonkar, C. J. Taylor and V. Kumar, "Robust stereo visual inertial odometry for fast autonomous flight," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 965-972, 2018. [Link]
  36. B. Schlotfeldt, D. Thakur, N. Atanasov, V. Kumar and G. J. Pappas, "Anytime planning for decentralized multirobot active information gathering," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1025-1032, 2018. [Link]
  37. S. W. Chen, S. S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C. J. Taylor and V. Kumar, "Counting apples and oranges with deep learning: A data-driven approach," IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 781-788, 2017. [Link]

Melvin Leok

  1. K. Long, K. Tran, M. Leok and N. Atanasov, "Safe Stabilizing Control for Polygonal Robots in Dynamic Elliptical Environments," preprint (arXiv:2310.00273), 2024. (To appear at the 2024 American Control Conference.)
  2. B. K. Tran, B. S. Southworth and M. Leok, "On Properties of Adjoint Systems for Evolutionary PDEs," preprint (arXiv:2404.02320), 2024.
  3. B. K. Tran and M. Leok, "Geometric methods for adjoint systems," Journal of Nonlinear Science, vol. 34, no. 1, 2024. [Link]
  4. W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M. E. Khan and M. Schmidt, "Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning," Fortieth International Conference on Machine Learning, 2023. [Link]
  5. V. Duruisseaux, T. P. Duong, M. Leok and N. Atanasov, "Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems," Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023. [Link]
  6. V. Duruisseaux and M. Leok, "Practical perspectives on symplectic accelerated optimization," Optimization Methods and Software, 2023. [Link]
  7. X. Shen and M. Leok, "Geometric exponential integrators," Journal of Computational Physics, 2019. [Link]
  8. M. Leok, "Variational discretizations of gauge field theories using group-equivariant interpolation," Foundations of Computational Mathematics, 2019. [Link]
  9. J. Hall and M. Leok, "Lie group spectral variational integrators," Foundations of Computational Mathematics, 2017. [Link]
  10. H. Parks and M. Leok, "Variational integrators for interconnected Lagrange-Dirac systems," Journal of Nonlinear Science, 2017. [Link]
  11. J. Vankerschaver, C. Liao and M. Leok, "Generating functionals and Lagrangian partial differential equations," Journal of Mathematical Physics, vol. 54, no. 8, 2013. [Link]

Yian Ma

  1. Y. Lin, Y. Ma, Y.-X. Wang, R. E. Redberg and Z. Bu, "Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy," The Twelfth International Conference on Learning Representations, 2024. [Link]
  2. X. Huang, H. Dong, H. Yifan, Y. Ma and T. Zhang, "Reverse Diffusion Monte Carlo," The Twelfth International Conference on Learning Representations, 2024. [Link]
  3. X. Huang, D. Zou, H. Dong, Y. Zhang, Y.-A. Ma and T. Zhang, "Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference," preprint (arXiv:2405.16387), 2024.
  4. X. Huang, D. Zou, H. Dong, Y. Ma and T. Zhang, "Faster Sampling via Stochastic Gradient Proximal Sampler," Forty-first International Conference on Machine Learning, 2024. [Link]
  5. R. Niu, D. Wu, K. Kim, Y.-A. Ma, D. Watson-Parris and R. Yu, "Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling," preprint (arXiv:2402.18846), 2024. (To appear at ICML 2024.)
  6. K. Kim, Y. Ma and J. Gardner, "Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?" International Conference on Artificial Intelligence and Statistics, 2024. [Link]
  7. F. X.-F. Ye, Y. Ma and H. Qian, "Estimate exponential memory decay in hidden Markov model and its applications to inference," Physica D: Nonlinear Phenomena, 2024. [Link]
  8. D. Wu, T. Ide, G. Kollias, J. Navratil, A. Lozano, N. Abe, Y. Ma and R. Yu, "Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes," International Conference on Artificial Intelligence and Statistics, 2024. [Link]
  9. D. Wu, N. L. Kuang, R. Niu, Y.-A. Ma and R. Yu, "Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization," preprint (arXiv:2407.00610), 2024.
  10. M. Hu, B. Li, Y.-A. Ma, Y. Lou and X. Yang, "A Gradient-Based Optimization Method Using the Koopman Operator," preprint (arXiv:2312.14361), 2023.
  11. K. Kim, J. Oh, K. Wu, Y. Ma and J. R. Gardner, "On the Convergence of Black-Box Variational Inference," Thirty-seventh Conference on Neural Information Processing Systems, 2023. [Link]
  12. K. Bhatia, Y.-A. Ma, A. D. Dragan, P. L. Bartlett and M. I. Jordan, "Bayesian robustness: A nonasymptotic viewpoint," Journal of the American Statistical Association, 2023. [Link]
  13. D. Wu, R. Niu, M. Chinazzi, A. Vespignani, Y.-A. Ma and R. Yu, "Deep Bayesian Active Learning for Accelerating Stochastic Simulation," Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023. [Link]
  14. D. Wu, R. Niu, M. Chinazzi, Y. Ma and R. Yu, "Disentangled Multi-Fidelity Deep Bayesian Active Learning," 40th International Conference on Machine Learning, 2023. [Link]
  15. B. Li, Y. Ma, J. N. Kutz and X. Yang, "The Adaptive Spectral Koopman Method for Dynamical Systems," SIAM Journal on Applied Dynamical Systems, vol. 22, no. 3, pp. 1523-1551, 2023. [Link]
  16. A. Roy, G. So and Y.-A. Ma, "Optimization on Pareto sets: On a theory of multi-objective optimization," preprint (arXiv:2308.02145), 2023.
  17. A. Karbasi, N. L. Kuang, Y. Ma and S. Mitra, "Langevin Thompson sampling with logarithmic communication: bandits and reinforcement learning," International Conference on Machine Learning, 2023. [Link]
  18. Y. Freund, Y.-A. Ma and T. Zhang, "When is the convergence time of Langevin algorithms dimension independent? A composite optimization viewpoint," The Journal of Machine Learning Research, vol. 23, no. 1, pp. 9604-9635, 2022. [Link]
  19. R. Shen, L. Gao and Y.-A. Ma, "On Optimal Early Stopping: Over-informative versus Under-informative Parametrization," preprint (arXiv:2202.09885), 2022.
  20. D. Wu, M. Chinazzi, A. Vespignani, Y.-A. Ma and R. Yu, "Multi-fidelity hierarchical neural processes," Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022. [Link]
  21. W. Mou, Y.-A. Ma, M. J. Wainwright, P. L. Bartlett and M. I. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm," The Journal of Machine Learning Research, vol. 22, no. 1, pp. 1919-1959, 2021. [Link]
  22. Y.-A. Ma, N. S. Chatterji, X. Cheng, N. Flammarion, P. L. Bartlett and M. I. Jordan, "Is there an analog of Nesterov acceleration for gradient-based MCMC?" , 2021. [Link]
  23. E. Mazumdar, A. Pacchiano, Y. Ma, P. L. Bartlett and M. I. Jordan, "On Thompson Sampling with Langevin Algorithms," preprint (arXiv:2002.10002), 2020.
  24. Y.-A. Ma, Y. Chen, C. Jin, N. Flammarion and M. I. Jordan, "Sampling can be faster than optimization," Proceedings of the National Academy of Sciences, vol. 116, no. 42, pp. 20881-20885, 2019. [Link]
  25. N. Chatterji, N. Flammarion, Y. Ma, P. Bartlett and M. Jordan, "On the theory of variance reduction for stochastic gradient Monte Carlo," International Conference on Machine Learning, 2018. [Link]

Arya Mazumdar

  1. H. Vardhan, A. Ghosh and A. Mazumdar, "An Improved Federated Clustering Algorithm with Model-based Clustering," Transactions on Machine Learning Research, 2024. [Link]
  2. A. Ghosh, A. Sankararaman, K. Ramchandran, T. Javidi and A. Mazumdar, "Competing Bandits in Non-Stationary Matching Markets," IEEE Transactions on Information Theory, 2024. [Link]
  3. A. Ghosh and A. Mazumdar, "Agnostic Learning of Mixed Linear Regressions with {EM} and {AM} Algorithms," Forty-first International Conference on Machine Learning, 2024. [Link]
  4. A. B. Kahng, A. Mazumdar, J. Reeves and Y. Wang, "The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics," AI Magazine, 2024. [Link]
  5. X. Yu, L. Cherkasova, H. Vardhan, Q. Zhao, E. Ekaireb, X. Zhang, A. Mazumdar and T. Rosing, "Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks," Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, 2023. [Link]
  6. H. Zhu, A. Ghosh and A. Mazumdar, "Optimal Compression of Unit Norm Vectors in the High Distortion Regime," 2023 IEEE International Symposium on Information Theory (ISIT), 2023. [Link]
  7. H. Zhu and A. Mazumdar, "Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization," International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023, 2023. [Link]
  8. H. Vardhan, A. Ghosh and A. Mazumdar, "A Convergent Federated Clustering Algorithm without Initial Condition," Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities, 2023. [Link]
  9. A. Ghosh, R. K. Maity and A. Mazumdar, "FED-CURE: A Robust Federated Learning Algorithm with Cubic Regularized Newton," Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities, 2023. [Link]
  10. A. Ghosh, A. Mazumdar, et al., "An Improved Algorithm for Clustered Federated Learning," preprint (arXiv:2210.11538), 2022.
  11. S. Pal, A. Mazumdar and V. Gandikota, "Support recovery of sparse signals from a mixture of linear measurements," Advances in Neural Information Processing Systems, 2021. [Link]
  12. A. Ghosh, R. K. Maity, S. Kadhe, A. Mazumdar and K. Ramchandran, "Communication-efficient and byzantine-robust distributed learning with error feedback," IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 3, pp. 942-953, 2021. [Link]
  13. V. Gandikota, A. Mazumdar and S. Pal, "Recovery of sparse linear classifiers from mixture of responses," Advances in Neural Information Processing Systems, 2020. [Link]
  14. S. Pal and A. Mazumdar, "Recovery of sparse signals from a mixture of linear samples," International Conference on Machine Learning, 2020. [Link]
  15. R. McKenna, R. K. Maity, A. Mazumdar and G. Miklau, "A workload-adaptive mechanism for linear queries under local differential privacy," preprint (arXiv:2002.01582), 2020.
  16. A. Ghosh, R. K. Maity and A. Mazumdar, "Distributed newton can communicate less and resist byzantine workers," Advances in Neural Information Processing Systems, 2020. [Link]
  17. A. Agarwal, L. Flodin and A. Mazumdar, "Linear programming approximations for index coding," IEEE Transactions on Information Theory, vol. 65, no. 9, pp. 5547-5564, 2019. [Link]

David Pan

  1. Z. Xiong, R. S. Rajarathnam and D. Z. Pan, "A Data-Driven, Congestion-Aware and Open-Source Timing-Driven FPGA Placer Accelerated by GPUs," The 32nd IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2024. (Collaboration with AMD. Best paper award.) [Link]
  2. Y. Lai, S. Lee, G. Chen, S. Poddar, M. Hu, D. Z. Pan and P. Luo, "AnalogCoder: Analog Circuit Design via Training-Free Code Generation," preprint (arXiv:2405.14918), 2024.
  3. Y. Lai, J. Liu, D. Z. Pan and P. Luo, "Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs," preprint (arXiv:2405.06758), 2024.
  4. S. Poddar, Y. Oh, Y. Lai, H. Zhu, B. Hwang and D. Z. Pan, "INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers," preprint (arXiv:2407.07346), 2024.
  5. S. Maji, H. Park, G. m. Hong, S. Poddar and D. Z. Pan, "Multi-Objective Optimization for Common-Centroid Placement of Analog Transistors," preprint (arXiv:2407.00817), 2024.
  6. R. Zhang, R. S. Rajarathnam, D. Z. Pan and F. Koushanfar, "ICMarks: A Robust Watermarking Framework for Integrated Circuit Physical Design IP Protection," preprint (arXiv:2404.18407), 2024.
  7. H. Chae, K. Zhu, B. Mutnury, Z. Jiang, D. De Araujo, D. Wallace, D. Winterberg, A. Klivans and D. Z. Pan, "ISOP-Yield: Yield-Aware Stack-Up Optimization for Advanced Package using Machine Learning," 29th Asia and South Pacific Design Automation Conference, 2024. [Link]
  8. H. Chae, K. Zhu, B. Mutnury, D. Wallace, D. Winterberg, D. De Araujo, J. Reddy, A. Klivans and D. Z. Pan, "ISOP+: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024. [Link]
  9. G. Chen, K. Zhu, S. Kim, H. Zhu, Y. Lai, B. Yu and D. Z. Pan, "LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation," preprint (arXiv:2406.05250), 2024.
  10. H. Chae, D. Z. Pan, A. Klivans, B. Mutnury, D. Winterberg, D. E. Wallace and A. Chada, "Method of Exploring HVM Process Corner Cases for Loss and Impedance in High Speed Designs," 2023 IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2023. [Link]
  11. A. F. Budak, K. Zhu and D. Z. Pan, "Practical Layout-Aware Analog/Mixed-Signal Design Automation with Bayesian Neural Networks," 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023. [Link]
  12. A. F. Budak, D. Smart, B. Swahn and D. Z. Pan, "APOSTLE: Asynchronously parallel optimization for sizing analog transistors using dnn learning," Proceedings of the 28th Asia and South Pacific Design Automation Conference, 2023. [Link]
  13. Z. Jiang, M. Liu, Z. Guo, S. Zhang, Y. Lin and D. Pan, "A Tale of EDA's Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation," Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022. [Link]
  14. R. S. Rajarathnam, M. B. Alawieh, Z. Jiang, M. Iyer and D. Z. Pan, "DREAMPlaceFPGA: An open-source analytical placer for large scale heterogeneous FPGAs using deep-learning toolkit," 27th Asia and South Pacific Design Automation Conference, 2022. (Collaboration with Mahesh Iyer of Intel.) [Link]
  15. K. Zhu, H. Chen, W. J. Turner, G. F. Kokai, P.-H. Wei, D. Z. Pan and H. Ren, "TAG: Learning circuit spatial embedding from layouts," Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022. [Link]
  16. K. Zhu, H. Chen, M. Liu, X. Tang, W. Shi, N. Sun and D. Z. Pan, "Generative-adversarial-network-guided well-aware placement for analog circuits," 27th Asia and South Pacific Design Automation Conference, 2022. [Link]
  17. K. Zhu, H. Chen, M. Liu and D. Z. Pan, "Automating analog constraint extraction: From heuristics to learning," 27th Asia and South Pacific Design Automation Conference, 2022. (Invited paper.) [Link]
  18. A. F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini, A. Goldie and D. Z. Pan, "Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives," 27th Asia and South Pacific Design Automation Conference, 2022. (Invited paper.) [Link]
  19. M. Rapp, H. Amrouch, Y. Lin, B. Yu, D. Z. Pan, M. Wolf and J. Henkel, "MLCAD: A survey of research in machine learning for CAD keynote paper," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 10, pp. 3162-3181, 2021. (Keynote paper.) [Link]
  20. M. B. Alawieh, Y. Lin, Z. Zhang, M. Li, Q. Huang and D. Z. Pan, "GAN-SRAF: Subresolution assist feature generation using generative adversarial networks," IEEE Transactions on CAD of Integrated Circuits and Systems, vol. 40, no. 2, pp. 373-385, 2020. [Link]
  21. H. Chen, M. Liu, B. Xu, K. Zhu, X. Tang, S. Li, Y. Lin, N. Sun and D. Z. Pan, "MAGICAL: An OpenSource Fully Automated Analog IC Layout System from Netlist to GDSII," IEEE Design & Test, vol. 38, no. 2, pp. 19-26, 2020. [Link]
  22. Y. Lin, S. Dhar, W. Li, H. Ren, B. Khailany and D. Z. Pan, "Dreamplace: Deep learning toolkit-enabled GPU acceleration for modern VSLI placement," Proceedings of the 56th Annual Design Automation Conference 2019, 2019. [Link]
  23. W. Ye, M. B. Alawieh, Y. Lin and D. Z. Pan, "LithoGAN: End-to-end lithography modeling with generative adversarial networks," Proceedings of the 56th Annual Design Automation Conference 2019, 2019. [Link]
  24. K. Zhu, M. Liu, Y. Lin, B. Xu, S. Li, X. Tang, N. Sun and D. Z. Pan, "GeniusRoute: A new analog routing paradigm using generative neural network guidance," 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019. [Link]

Jodi Reeves

  1. A. B. Kahng, A. Mazumdar, J. Reeves and Y. Wang, "The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics," AI Magazine, 2024. [Link]
  2. B. D. Radhakrishnan, J. Reeves, J. J. Ninteman and C. Hahm, "Sustainability Intelligence: Emergence and Use of Big Data for Sustainable Urban Planning," 2016 ASEE Annual Conference & Exposition, 2016. [Link]
  3. A. W. Lo, J. Reeves, P. Jenkins and R. Parkman, "Retention Initiatives for Working Adult Students in Accelerated Programs," Journal of Research in Innovative Teaching, vol. 9, no. 1, 2016. [Link]
  4. B. Arnold and J. Reeves, "Translating best practices for student engagement to online STEAM courses," Proceedings of the 2014 American Society for Engineering Education Zone IV Conference, 2014. [Link]

Alejandro Ribeiro

  1. Y. B. Uslu, R. Doostnejad, A. Ribeiro and N. NaderiAlizadeh, "Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach," preprint (arXiv:2405.05748), 2024.
  2. X. Chen, N. NaderiAlizadeh, A. Ribeiro and S. S. Bidokhti, "Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks," preprint (arXiv:2404.03227), 2024.
  3. S. Khalafi, S. Sihag and A. Ribeiro, "Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks," Forty-first International Conference on Machine Learning, 2024. [Link]
  4. I. Hounie, J. Porras-Valenzuela and A. Ribeiro, "Loss Shaping Constraints for Long-Term Time Series Forecasting," Forty-first International Conference on Machine Learning, 2024. [Link]
  5. J. Elenter, N. NaderiAlizadeh, T. Javidi and A. Ribeiro, "Primal-Dual Continual Learning: Stability and Plasticity through Lagrange Multipliers," preprint (arXiv:2310.00154), 2023.
  6. J. Cerviño, L. Ruiz and A. Ribeiro, "Learning by Transference: Training Graph Neural Networks on Growing Graphs," IEEE Transactions on Signal Processing, 2023. [Link]
  7. J. Cerviño, L. F. Chamon, B. D. Haeffele, R. Vidal and A. Ribeiro, "Learning globally smooth functions on manifolds," International Conference on Machine Learning, 2023. [Link]
  8. I. Spasojevic, X. Liu, A. Prabhu, A. Ribeiro, G. J. Pappas and V. Kumar, "Robust localization of aerial vehicles via active control of identical ground vehicles," International Conference on Intelligent Robots and Systems, 2023. [Link]
  9. I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," Robotics: Science and Systems, 2023. [Link]
  10. I. Hounie, L. F. O. Chamon and A. Ribeiro, "Automatic Data Augmentation via Invariance-Constrained Learning," Proceedings of the 40th International Conference on Machine Learning, 2023. [Link]
  11. I. Hounie, J. Elenter and A. Ribeiro, "Neural Networks with Quantization Constraints," 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. [Link]
  12. I. Hounie, A. Ribeiro and L. F. O. Chamon, "Resilient Constrained Learning," preprint (arXiv:2306.02426), 2023.
  13. I. Boero, I. Spasojevic, M. Del Castillo, G. Pappas, V. Kumar and A. Ribeiro, "Navigation with shadow prices to optimize multi-commodity flow rates," 62nd IEEE Conference on Decision and Control (CDC), 2023. [Link]
  14. Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Self-consistency of the Fokker Planck equation," Conference on Learning Theory, 2022. [Link]
  15. D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5552-5559, 2022. [Link]
  16. E. Tolstaya, F. Gama, J. Paulos, G. Pappas, V. Kumar and A. Ribeiro, "Learning decentralized controllers for robot swarms with graph neural networks," Conference on Robot Learning, 2020. [Link]
  17. A. G. Marques, S. Segarra, G. Leus and A. Ribeiro, "Stationary graph processes and spectral estimation," IEEE Transactions on Signal Processing, vol. 65, no. 22, pp. 5911-5926, 2017. [Link]
  18. S. Segarra, G. Mateos, A. G. Marques and A. Ribeiro, "Blind identification of graph filters," IEEE Transactions on Signal Processing, vol. 65, no. 5, pp. 1146-1159, 2016. [Link]
  19. S. Segarra, A. G. Marques, G. Leus and A. Ribeiro, "Reconstruction of graph signals through percolation from seeding nodes," IEEE Transactions on Signal Processing, vol. 64, no. 16, pp. 4363-4378, 2016. [Link]
  20. S. Segarra, A. G. Marques and A. Ribeiro, "Distributed linear network operators using graph filters," preprint (arXiv:1510.03947), 2015.
  21. A. G. Marques, S. Segarra, G. Leus and A. Ribeiro, "Sampling of graph signals with successive local aggregations," IEEE Transactions on Signal Processing, vol. 64, no. 7, pp. 1832-1843, 2015. [Link]

Tajana Rosing

  1. Y. Pan, M. Zhou, C. Lee, Z. Li, R. Kushwah, V. Narayanan and T. Rosing, "PRIMATE: Processing in Memory Acceleration for Dynamic Token-pruning Transformers," 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 2024. [Link]
  2. X. Yu, A. Thomas, I. G. Moreno, L. Gutierrez and T. Rosing, "Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing," ACM/IEEE International Conference on Information Processing in Sensor Networks, 2024. [Link]
  3. X. Yu, T. Rosing and Y. Guo, "EVOLVE: Enhancing Unsupervised Continual Learning with Multiple Experts," Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024. [Link]
  4. W. Xu, P.-K. Hsu, N. Moshiri, S. Yu and T. Rosing, "HyperGen: Compact and Efficient Genome Sketching using Hyperdimensional Vectors," preprint, 2024. (Submitted to Bioinformatics.) [Link]
  5. I. G. Moreno, X. Yu and T. Rosing, "KalmanHD: Robust On-Device Time Series Forecasting with Hyperdimensional Computing," 29th Asia and South Pacific Design Automation Conference, 2024. [Link]
  6. Y. Nam, M. Zhou, S. Gupta, G. De Micheli, R. Cammarota, C. Wilkerson, D. Micciancio and T. Rosing, "Efficient Machine Learning on Encrypted Data Using Hyperdimensional Computing," IEEE/ACM International Symposium on Low Power Electronics and Design, 2023. [Link]
  7. X. Yu, L. Cherkasova, H. Vardhan, Q. Zhao, E. Ekaireb, X. Zhang, A. Mazumdar and T. Rosing, "Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks," Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, 2023. [Link]
  8. W. Xu, V. Swaminathan, S. Pinge, S. Fuhrman and T. Rosing, "HyperMetric: Robust Hyperdimensional Computing on Error-prone Memories using Metric Learning," IEEE 41st International Conference on Computer Design, 2023. [Link]
  9. T. Zhang, A. González, N. Moshiri, R. Knight and T. Rosing, "GenoMiX: Accelerated Simultaneous Analysis of Human Genomics, Microbiome Metagenomics, and Viral Sequences," 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2023. [Link]
  10. Q. Zhao, X. Yu and T. Rosing, "Attentive Multimodal Learning on Sensor Data using Hyperdimensional Computing," Proceedings of the 22nd International Conference on Information Processing in Sensor Networks, 2023. [Link]
  11. Q. Zhao, A. Thomas, A. Brin, X. Yu and T. Rosing, "Unleashing Hyperdimensional Computing with Nyström Method based Encoding," preprint, 2023. [Link]
  12. M. Zhou, Y. Nam, P. Gangwar, W. Xu, A. Dutta, K. Subramanyam, C. Wilkerson, R. Cammarota, S. Gupta and T. Rosing, "FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption," preprint (arXiv:2311.16293), 2023. (Collaboration with at Chris Wilkerson and Rosario Cammarota of Intel and Saransh Gupta of IBM.)
  13. M. Timken, O. Gungor, T. Rosing and B. Aksanli, "Analysis of Machine Learning Algorithms for Cyber Attack Detection in SCADA Power Systems," International Conference on Smart Applications, Communications and Networking, 2023. [Link]
  14. J. Kang, W. Xu, W. Bittremieux, N. Moshiri and T. Rosing, "Accelerating open modification spectral library searching on tensor core in high-dimensional space," Bioinformatics, vol. 39, no. 7, 2023. [Link]
  15. D. Liu, K. Ergun and T. Š. Rosing, "Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification," IEEE International Conference on Acoustics, Speech and Signal Processing, 2023. [Link]
  16. Q. Zhao, K. Lee, J. Liu, M. Huzaifa, X. Yu and T. Rosing, "FedHD: Federated learning with hyperdimensional computing," Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, 2022. [Link]
  17. E. Ekaireb, X. Yu, K. Ergun, Q. Zhao, K. Lee, M. Huzaifa and T. Rosing, "ns3-fl: Simulating Federated Learning with ns-3," Proceedings of the 2022 Workshop on ns-3, 2022. [Link]

Daniel Spielman

  1. D. Kunisky, D. A. Spielman and X. Yu, "Inference of rankings planted in random tournaments," preprint (arXiv:2407.16597), 2024.
  2. R. Kyng, Y. T. Lee, R. Peng, S. Sachdeva and D. A. Spielman, "Sparsified cholesky and multigrid solvers for connection laplacians," Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, 2016. [Link]
  3. D. A. Spielman and S.-H. Teng, "Nearly linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems," SIAM Journal on Matrix Analysis and Applications, vol. 35, no. 3, pp. 835-885, 2014. [Link]
  4. D. A. Spielman and S.-H. Teng, "A local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning," SIAM Journal on computing, vol. 42, no. 1, pp. 1-26, 2013. [Link]
  5. P. Christiano, J. A. Kelner, A. Madry, D. A. Spielman and S.-H. Teng, "Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs," Proceedings of the forty-third annual ACM symposium on Theory of computing, 2011. [Link]
  6. D. A. Spielman and N. Srivastava, "Graph sparsification by effective resistances," Proceedings of the 40th Annual ACM Symposium on Theory of Computing, 2008.

Suvrit Sra

  1. K. Ahn, A. Jadbabaie and S. Sra, "How to Escape Sharp Minima with Random Perturbations," Forty-first International Conference on Machine Learning, 2024. [Link]
  2. Y. Tian, K. Zhang, R. Tedrake and S. Sra, "Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?" Learning for Dynamics and Control Conference, 2023. [Link]
  3. P. Zhang, J. Zhang and S. Sra, "Sion’s Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm," SIAM Journal on Optimization, vol. 33, no. 4, pp. 2885-2908, 2023. [Link]
  4. K. Ahn, X. Cheng, M. Song, C. Yun, A. Jadbabaie and S. Sra, "Linear attention is (maybe) all you need (to understand transformer optimization)," NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 2023. [Link]
  5. K. Ahn, X. Cheng, H. Daneshmand and S. Sra, "Transformers learn to implement preconditioned gradient descent for in-context learning," Thirty-seventh Conference on Neural Information Processing Systems, 2023. [Link]
  6. D. X. Wu, C. Yun and S. Sra, "On the Training Instability of Shuffling SGD with Batch Normalization," Proceedings of the 40th International Conference on Machine Learning, 2023. (Work with David Wu, co-supervised by the other authors.) [Link]
  7. X. Cheng, J. Zhang and S. Sra, "Theory and Algorithms for Diffusion Processes on Riemannian Manifolds," preprint (arXiv:2204.13665), 2022.
  8. X. Cheng, J. Zhang and S. Sra, "Efficient Sampling on Riemannian Manifolds via Langevin MCMC," Advances in Neural Information Processing Systems, 2022. [Link]
  9. P. H. Zadeh and S. Sra, "Introducing discrepancy values of matrices with application to bounding norms of commutators," Linear Algebra and its Applications, 2022. (Work with PhD student Pourya Zadeh supervised by Sra.) [Link]
  10. M. Weber and S. Sra, "On a class of geodesically convex optimization problems solved via Euclidean MM methods," preprint (arXiv:2206.11426), 2022.
  11. M. Weber and S. Sra, "Computing Brascamp-Lieb Constants through the lens of Thompson Geometry," preprint (arXiv:2208.05013), 2022.
  12. J. Jin and S. Sra, "Understanding Riemannian acceleration via a proximal extragradient framework," Conference on Learning Theory, 2022. [Link]
  13. D. Lim, J. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron and S. Jegelka, "Sign and basis invariant networks for spectral graph representation learning," preprint (arXiv:2202.13013), 2022. (Spotlight/notable top 25%)
  14. C. Yun, S. Rajput and S. Sra, "Minibatch vs Local {SGD} with Shuffling: Tight Convergence Bounds and Beyond," International Conference on Learning Representations, 2022. [Link]
  15. J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka and S. Sra, "Can contrastive learning avoid shortcut solutions?" Advances in neural information processing systems, 2021. [Link]
  16. A. Jadbabaie, H. Mania, D. Shah and S. Sra, "Time varying regression with hidden linear dynamics," preprint (arXiv:2112.14862), 2021. (Work with postdoc Horia Mania co-supervised by the other named authors.)
  17. S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," preprint (arXiv:2112.00056), 2021.
  18. K. Ahn and S. Sra, "From Nesterov’s estimate sequence to Riemannian acceleration," Conference on Learning Theory, 2020. [Link]
  19. S. J. Reddi, A. Hefny, S. Sra, B. Poczos and A. Smola, "Stochastic variance reduction for nonconvex optimization," International Conference on Machine Learning, 2016. [Link]
  20. H. Zhang, S. J Reddi and S. Sra, "Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds," Advances in Neural Information Processing Systems, vol. 29, 2016. [Link]
  21. H. Zhang and S. Sra, "First-order methods for geodesically convex optimization," Conference on Learning Theory, 2016. [Link]
  22. S. Sra and R. Hosseini, "Conic geometric optimization on the manifold of positive definite matrices," SIAM Journal on Optimization, vol. 25, no. 1, pp. 713-739, 2015. [Link]

Hao Su

  1. Y. Feng, Y. Shang, X. Feng, L. Lan, S. Zhe, T. Shao, H. Wu, K. Zhou, H. Su, C. Jiang and Y. Yang, "ElastoGen: 4D Generative Elastodynamics," preprint (arXiv:2405.15056), 2024.
  2. N. Hansen, H. Su and X. Wang, "TD-MPC2: Scalable, Robust World Models for Continuous Control," International Conference on Learning Representations, 2024. (ICLR 2024 spotlight.) [Link]
  3. M. V. T, P. Wang, Z. Fan, Z. Wang, H. Su and R. Ramamoorthi, "Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D," Computer Vision and Pattern Recognition Conference, 2024. [Link]
  4. Z. Jia, F. Liu, V. Thumuluri, L. Chen, Z. Huang and H. Su, "Chain-of-Thought Predictive Control," Workshop on Reincarnating Reinforcement Learning at ICLR 2023, 2023. [Link]
  5. Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "DexPoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation," Conference on Robot Learning, 2023. [Link]
  6. S. Li, Z. Huang, T. Chen, T. Du, H. Su, J. B. Tenenbaum and C. Gan, "DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics," preprint (arXiv:2304.03223), 2023.
  7. J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, H. Su, Y. Tang, S. Tao, X. Wei, Y. Yao, et al., "ManiSkill2: A unified benchmark for generalizable manipulation skills," preprint (arXiv:2302.04659), 2023.
  8. Y. Xu, N. Hansen, Z. Wang, Y.-C. Chan, H. Su and Z. Tu, "On the feasibility of cross-task transfer with model-based reinforcement learning," preprint (arXiv:2210.10763), 2022.
  9. Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation," Deep Reinforcement Learning Workshop NeurIPS 2022, 2022. [Link]
  10. Y. Qin, H. Su and X. Wang, "From one hand to multiple hands: Imitation learning for dexterous manipulation from single-camera teleoperation," IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10873-10881, 2022. [Link]
  11. N. Hansen, Z. Yuan, Y. Ze, T. Mu, A. Rajeswaran, H. Su, H. Xu and X. Wang, "On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline," preprint (arXiv:2212.05749), 2022.
  12. N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar and A. Rajeswaran, "MoDem: Accelerating visual model-based reinforcement learning with demonstrations," preprint (arXiv:2212.05698), 2022.
  13. N. Hansen, X. Wang and H. Su, "Temporal difference learning for model predictive control," preprint (arXiv:2203.04955), 2022.
  14. J. Gu, D. S. Chaplot, H. Su and J. Malik, "Multi-skill mobile manipulation for object rearrangement," preprint (arXiv:2209.02778), 2022. (Spotlight.)
  15. N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation," Advances in Neural Information Processing Systems, 2021. [Link]
  16. Z. Jia and H. Su, "Information-theoretic local minima characterization and regularization," International Conference on Machine Learning, 2020. [Link]
  17. T. Mu, J. Gu, Z. Jia, H. Tang and H. Su, "Refactoring policy for compositional generalizability using self-supervised object proposals," Advances in Neural Information Processing Systems, 2020. [Link]
  18. H. Tang, Z. Huang, J. Gu, B.-L. Lu and H. Su, "Towards scale-invariant graph-related problem solving by iterative homogeneous gnns," Advances in Neural Information Processing Systems, 2020. [Link]
  19. Z. Huang, F. Liu and H. Su, "Mapping state space using landmarks for universal goal reaching," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  20. C. R. Qi, H. Su, K. Mo and L. J. Guibas, "Pointnet: Deep learning on point sets for 3d classification and segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. [Link]

Camillo J. Taylor

  1. I. D. Miller, F. Cladera, T. Smith, C. J. Taylor and V. Kumar, "Air-Ground Collaboration with SPOMP: Semantic Panoramic Online Mapping and Planning," IEEE Transactions on Field Robotics, 2024. [Link]
  2. H. Sanghvi, S. Folk and C. J. Taylor, "OCCAM: Online Continuous Controller Adaptation with Meta-Learned Models," preprint (arXiv:2406.17620), 2024.
  3. F. Cladera, I. D. Miller, Z. Ravichandran, V. Murali, J. Hughes, M. A. Hsieh, C. J. Taylor and V. Kumar, "Challenges and Opportunities for Large-Scale Exploration with Air-Ground Teams using Semantics," preprint (arXiv:2405.07169), 2024.
  4. B. Jiang, Y. Xie, X. Wang, W. J. Su, C. J. Taylor and T. Mallick, "Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey," preprint (arXiv:2406.00252), 2024.
  5. X. Liu, A. Prabhu, F. Cladera, I. D. Miller, L. Zhou, C. J. Taylor and V. Kumar, "Active metric-semantic mapping by multiple aerial robots," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Link]
  6. F. Cladera, Z. Ravichandran, I. D. Miller, M. A. Hsieh, C. J. Taylor and V. Kumar, "Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications," preprint (arXiv:2309.15975), 2023.
  7. X. Liu, S. W. Chen, G. V. Nardari, C. Qu, F. C. Ojeda, C. J. Taylor and V. Kumar, "Challenges and opportunities for autonomous micro-UAVs in precision agriculture," IEEE Micro, vol. 42, no. 1, pp. 61-68, 2022. [Link]
  8. X. Liu, G. V. Nardari, F. C. Ojeda, Y. Tao, A. Zhou, T. Donnelly, C. Qu, S. W. Chen, R. A. Romero, C. J. Taylor, et al., "Large-scale autonomous flight with real-time semantic slam under dense forest canopy," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5512-5519, 2022. [Link]
  9. H. Sanghvi and C. J. Taylor, "Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models," 2022 International Conference on Robotics and Automation (ICRA), 2022. [Link]
  10. T. Nguyen, K. Mohta, C. J. Taylor and V. Kumar, "Vision-based multi-MAV localization with anonymous relative measurements using coupled probabilistic data association filter," 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. [Link]
  11. X. Liu, S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das and V. Kumar, "Robust fruit counting: Combining deep learning, tracking, and structure from motion," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [Link]
  12. K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y. Mulgaonkar, C. J. Taylor and V. Kumar, "Robust stereo visual inertial odometry for fast autonomous flight," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 965-972, 2018. [Link]
  13. S. W. Chen, S. S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C. J. Taylor and V. Kumar, "Counting apples and oranges with deep learning: A data-driven approach," IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 781-788, 2017. [Link]

Nisheeth Vishnoi

  1. O. Mangoubi and N. K. Vishnoi, "Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers," The Twelfth International Conference on Learning Representations, 2024. [Link]
  2. O. Mangoubi and N. K. Vishnoi, "Sampling from Structured Log-Concave Distributions via a Soft-Threshold Dikin Walk," Advances in Neural Information Processing Systems, 2023. [Link]
  3. O. Mangoubi and N. K. Vishnoi, "Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations," 36th Annual Conference on Learning Theory, 2023. [Link]
  4. N. Boehmer, L. E. Celis, L. Huang, A. Mehrotra and N. K. Vishnoi, "Subset selection based on multiple rankings in the presence of bias: Effectiveness of fairness constraints for multiwinner voting score functions," International Conference on Machine Learning, 2023. [Link]
  5. L. E. Celis, A. Kumar, A. Mehrotra and N. K. Vishnoi, "Bias in Evaluation Processes: An Optimization-Based Model," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
  6. A. Mehrotra and N. K. Vishnoi, "Maximizing Submodular Functions for Recommendation in the Presence of Biases," Proceedings of the ACM Web Conference 2023, 2023. [Link]
  7. V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent Min-Max Optimization Algorithm," International Conference on Machine Learning, 2022. [Link]
  8. O. Mangoubi and N. Vishnoi, "Sampling from log-concave distributions with infinity-distance guarantees," Advances in Neural Information Processing Systems, 2022. [Link]
  9. O. Mangoubi and N. Vishnoi, "Re-analyze Gauss: Bounds for private matrix approximation via Dyson Brownian motion," Advances in Neural Information Processing Systems, 2022. [Link]
  10. A. Mehrotra, B. S. Pradelski and N. K. Vishnoi, "Selection in the presence of implicit bias: the advantage of intersectional constraints," Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022. [Link]
  11. A. Mehrotra and N. Vishnoi, "Fair ranking with noisy protected attributes," Advances in Neural Information Processing Systems, 2022. [Link]
  12. J. Leake and N. K. Vishnoi, "On the computability of continuous maximum entropy distributions with applications," Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020. [Link]
  13. O. Mangoubi and N. K. Vishnoi, "Nonconvex sampling with the Metropolis-adjusted Langevin algorithm," Conference on Learning Theory, 2019. [Link]
  14. O. Mangoubi and N. K. Vishnoi, "Faster polytope rounding, sampling, and volume computation via a sub-linear ball walk," 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), 2019. [Link]
  15. H. Lee, O. Mangoubi and N. Vishnoi, "Online sampling from log-concave distributions," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  16. O. Mangoubi and N. Vishnoi, "Dimensionally tight bounds for second-order Hamiltonian Monte Carlo," Advances in Neural Information Processing Systems, vol. 31, 2018. [Link]

Xiaolong Wang

  1. Y. Lin, Y. Ma, Y.-X. Wang, R. E. Redberg and Z. Bu, "Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy," The Twelfth International Conference on Learning Representations, 2024. [Link]
  2. T. Wang, D. Bhatt, X. Wang and N. Atanasov, "Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignment," preprint (arXiv:2406.01968), 2024.
  3. R. Ding, Y. Qin, J. Zhu, C. Jia, S. Yang, R. Yang, X. Qi and X. Wang, "Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning," preprint (arXiv:2407.03162), 2024.
  4. N. Hansen, H. Su and X. Wang, "TD-MPC2: Scalable, Robust World Models for Continuous Control," International Conference on Learning Representations, 2024. (ICLR 2024 spotlight.) [Link]
  5. B. Jiang, Y. Xie, X. Wang, W. J. Su, C. J. Taylor and T. Mallick, "Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey," preprint (arXiv:2406.00252), 2024.
  6. A.-C. Cheng, H. Yin, Y. Fu, Q. Guo, R. Yang, J. Kautz, X. Wang and S. Liu, "SpatialRGPT: Grounded Spatial Reasoning in Vision Language Model," preprint (arXiv:2406.01584), 2024.
  7. Z.-H. Yin, B. Huang, Y. Qin, Q. Chen and X. Wang, "Rotating without seeing: Towards in-hand dexterity through touch," Robotics: Science and Systems, 2023. [Link]
  8. Y.-H. Wu, J. Wang and X. Wang, "Learning generalizable dexterous manipulation from human grasp affordance," Conference on Robot Learning, 2023. [Link]
  9. Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "DexPoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation," Conference on Robot Learning, 2023. [Link]
  10. R. Yang, G. Yang and X. Wang, "Neural volumetric memory for visual locomotion control," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. [Link]
  11. J. Ye, J. Wang, B. Huang, Y. Qin and X. Wang, "Learning continuous grasping function with a dexterous hand from human demonstrations," IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2882-2889, 2023. [Link]
  12. J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang and S. De Mello, "Open-Vocabulary Panoptic Segmentation With Text-to-Image Diffusion Models," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. (Collaboration with Sifei Liu, Arash Vahdat, Wonmin Byeon, and Shalini De Mello of NVIDIA.) [Link]
  13. X. Wang, N. Heydaribeni, F. Koushanfar and T. Javidi, "Federated Certainty Equivalence Control for Linear Gaussian Systems with Unknown Decoupled Dynamics and Quadratic Common Cost," 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2023. [Link]
  14. Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu and X. Wang, "DexMV: Imitation learning for dexterous manipulation from human videos," European Conference on Computer Vision, 2022. [Link]
  15. Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation," Deep Reinforcement Learning Workshop NeurIPS 2022, 2022. [Link]
  16. Y. Qin, H. Su and X. Wang, "From one hand to multiple hands: Imitation learning for dexterous manipulation from single-camera teleoperation," IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10873-10881, 2022. [Link]
  17. R. Yang, M. Zhang, N. Hansen, H. Xu and X. Wang, "Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers," International Conference on Learning Representations, 2022. [Link]
  18. R. Jangir, N. Hansen, S. Ghosal, M. Jain and X. Wang, "Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3046-3053, 2022. [Link]
  19. N. Hansen, Z. Yuan, Y. Ze, T. Mu, A. Rajeswaran, H. Su, H. Xu and X. Wang, "On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline," preprint (arXiv:2212.05749), 2022.
  20. N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar and A. Rajeswaran, "MoDem: Accelerating visual model-based reinforcement learning with demonstrations," preprint (arXiv:2212.05698), 2022.
  21. N. Hansen, X. Wang and H. Su, "Temporal difference learning for model predictive control," preprint (arXiv:2203.04955), 2022.
  22. C. S. Imai, M. Zhang, Y. Zhang, M. Kierebinski, R. Yang, Y. Qin and X. Wang, "Vision-guided quadrupedal locomotion in the wild with multi-modal delay randomization," IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022. [Link]
  23. X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-peer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172-182, 2022. [Link]
  24. Y. Li, M. Hao, Z. Di, N. B. Gundavarapu and X. Wang, "Test-time personalization with a transformer for human pose estimation," Advances in Neural Information Processing Systems, 2021. [Link]
  25. N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation," Advances in Neural Information Processing Systems, 2021. [Link]
  26. J. Wang, H. Xu, M. Narasimhan and X. Wang, "Multi-person 3D motion prediction with multi-range transformers," Advances in Neural Information Processing Systems, 2021. [Link]
  27. R. Yang, H. Xu, Y. Wu and X. Wang, "Multi-task reinforcement learning with soft modularization," Advances in Neural Information Processing Systems, 2020. [Link]
  28. Q. Long, Z. Zhou, A. Gupta, F. Fang, Y. Wu and X. Wang, "Evolutionary population curriculum for scaling multi-agent reinforcement learning," preprint (arXiv:2003.10423), 2020.
  29. W. Yang, X. Wang, A. Farhadi, A. Gupta and R. Mottaghi, "Visual semantic navigation using scene priors," preprint (arXiv:1810.06543), 2018.
  30. X. Wang, R. Girshick, A. Gupta and K. He, "Non-local neural networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. [Link]
  31. X. Wang and A. Gupta, "Videos as space-time region graphs," Proceedings of the European Conference on Computer Vision (ECCV), 2018. [Link]

Yusu Wang

  1. Z. Luo, T. S. Hy, P. Tabaghi, M. Defferrard, E. Rezaei, R. M. Carey, R. Davis, R. Jain and Y. Wang, "DE-HNN: An effective neural model for Circuit Netlist representation," International Conference on Artificial Intelligence and Statistics, 2024. (Collaboration with Rajeev Jain's team at Qualcomm.) [Link]
  2. X. Wu, A. Ajorlou, Y. Wang, S. Jegelka and A. Jadbabaie, "On the Role of Attention Masks and LayerNorm in Transformers," preprint (arXiv:2405.18781), 2024.
  3. T. Papamarkou, T. Birdal, M. Bronstein, G. Carlsson, J. Curry, Y. Gao, M. Hajij, R. Kwitt, P. Liò, P. D. Lorenzo, V. Maroulas, N. Miolane, F. Nasrin, K. N. Ramamurthy, B. Rieck, S. Scardapane, M. T. Schaub, P. Veličković, B. Wang, Y. Wang, G.-W. Wei and G. Zamzmi, "Position Paper: Challenges and Opportunities in Topological Deep Learning," preprint (arXiv:2402.08871), 2024.
  4. T. Brugère, Z. Wan and Y. Wang, "Distances for Markov chains, and their differentiation," International Conference on Algorithmic Learning Theory, 2024. [Link]
  5. S. Gupta, C. Wang, Y. Wang, T. Jaakkola and S. Jegelka, "In-Context Symmetries: Self-Supervised Learning through Contextual World Models," ICML 2024 Workshop on In-Context Learning, 2024. [Link]
  6. S. Chen, P. Tabaghi and Y. Wang, "Learning ultrametric trees for optimal transport regression," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 18, pp. 20657-20665, 2024. [Link]
  7. P. Tabaghi and Y. Wang, "Universal Representation of Permutation-Invariant Functions on Vectors and Tensors," International Conference on Algorithmic Learning Theory, 2024. [Link]
  8. M. Black, Z. Wan, G. Mishne, A. Nayyeri and Y. Wang, "Comparing Graph Transformers via Positional Encodings," preprint (arXiv:2402.14202), 2024.
  9. G. Ma, Y. Wang, D. Lim, S. Jegelka and Y. Wang, "A Canonization Perspective on Invariant and Equivariant Learning," preprint (arXiv:2405.18378), 2024.
  10. C. Zhou, R. Yu and Y. Wang, "On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers," International Conference on Artificial Intelligence and Statistics, 2024. [Link]
  11. Y. Wang, Y. Wu, Z. Wei, S. Jegelka and Y. Wang, "A Theoretical Understanding of Self-Correction through In-context Alignment," ICML 2024 Workshop on In-Context Learning, 2024. [Link]
  12. Y. Wang, K. Hu, S. Gupta, Z. Ye, Y. Wang and S. Jegelka, "Understanding the Role of Equivariance in Self-supervised Learning," ICML 2024 Workshop on Theoretical Foundations of Foundation Models, 2024. [Link]
  13. A. B. Kahng, R. R. Nerem, Y. Wang and C.-Y. Yang, "NN-Steiner: A mixed neural-algorithmic approach for the rectilinear Steiner minimum tree problem," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 12, pp. 13022-13030, 2024. [Link]
  14. A. B. Kahng, A. Mazumdar, J. Reeves and Y. Wang, "The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics," AI Magazine, 2024. [Link]
  15. S. Chen and Y. Wang, "Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions," Conference on Neural Information Processing Systems, 2023. [Link]
  16. M. Black, Z. Wan, A. Nayyeri and Y. Wang, "Understanding Oversquashing in GNNs through the Lens of Effective Resistance," Proceedings of the 40th International Conference on Machine Learning, 2023. [Link]
  17. G. Mishne, Z. Wan, Y. Wang and S. Yang, "The numerical stability of hyperbolic representation learning," International Conference on Machine Learning, 2023. [Link]
  18. C.-K. Cheng, A. B. Kahng, S. Kundu, Y. Wang and Z. Wang, "Assessment of Reinforcement Learning for Macro Placement," Proceedings of the 2023 International Symposium on Physical Design, 2023. (Invited paper.) [Link]
  19. C.-K. Cheng, A. B. Kahng, B. Lin, Y. Wang and D. Yoon, "Gear-Ratio-Aware Standard Cell Layout Framework for DTCO Exploration," Association for Computing Machinery, 2023. [Link]
  20. C. Cai, T. S. Hy, R. Yu and Y. Wang, "On the connection between mpnn and graph transformer," International Conference on Machine Learning, 2023.
  21. A. Cloninger, G. Mishne, A. Oslandsbotn, S. J. Robertson, Z. Wan and Y. Wang, "Random Walks, Conductance, and Resistance for the Connection Graph Laplacian," preprint (arXiv:2308.09690), 2023.
  22. A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," 39th International Symposium on Computational Geometry (SoCG 2023), 2023. [Link]
  23. S. Chen, S. Lim, F. Mémoli, Z. Wan and Y. Wang, "Weisfeiler-Lehman meets Gromov-Wasserstein," International Conference on Machine Learning, 2022. [Link]
  24. F. Mémoli, Z. Wan and Y. Wang, "Persistent Laplacians: Properties, algorithms and implications," SIAM Journal on Mathematics of Data Science, vol. 4, no. 2, pp. 858-884, 2022. [Link]
  25. C. Cai and Y. Wang, "Convergence of invariant graph networks," International Conference on Machine Learning, 2022. [Link]
  26. E. McCarty, Q. Zhao, A. Sidiropoulos and Y. Wang, "NN-Baker: A neural-network infused algorithmic framework for optimization problems on geometric intersection graphs," Advances in Neural Information Processing Systems, 2021. [Link]
  27. Q. Zhao and Y. Wang, "Learning metrics for persistence-based summaries and applications for graph classification," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  28. T. K. Dey, J. Wang and Y. Wang, "Graph reconstruction by discrete Morse theory," preprint (arXiv:1803.05093), 2018.
  29. J. Eldridge, M. Belkin and Y. Wang, "Unperturbed: Spectral analysis beyond Davis-Kahan," Algorithmic Learning Theory, 2018. [Link]
  30. A. Sidiropoulos, D. Wang and Y. Wang, "Metric embeddings with outliers," Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms, 2017. [Link]
  31. J. Eldridge, M. Belkin and Y. Wang, "Graphons, mergeons, and so on!" Advances in Neural Information Processing Systems, vol. 29, 2016. [Link]