Publications by TILOS Faculty

Nikolay Atanasov

  • 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.
  • 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]
  • 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]
  • 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]
  • E. Sebastian, T. Duong, N. Atanasov, E. Montijano and C. Sagués, "Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks," preprint (arXiv:2307.04374), 2023.
  • 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]
  • 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.
  • 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.
  • Z. Li, T. Duong and N. Atanasov, "Safe autonomous navigation for systems with learned SE(3) Hamiltonian dynamics," Learning for Dynamics and Control Conference, 2022. [Link]
  • 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]
  • 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]
  • T. Duong and N. Atanasov, "Hamiltonian-based neural ODE networks on the SE(3) manifold for dynamics learning and control," preprint (arXiv:2106.12782), 2021.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • 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]
  • 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.
  • 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

  • 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.
  • A. Radhakrishnan, M. Belkin and D. Drusvyatskiy, "Linear Recursive Feature Machines provably recover low-rank matrices," preprint (arXiv:2401.04553), 2024.
  • 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.
  • L. Hui, M. Belkin and S. Wright, "Cut your losses with squentropy," preprint (arXiv:2302.03952), 2023.
  • 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.
  • 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]
  • D. Beaglehole, A. Radhakrishnan, P. Pandit and M. Belkin, "Mechanism of feature learning in convolutional neural networks," preprint (arXiv:2309.00570), 2023.
  • C. Liu, D. Drusvyatskiy, M. Belkin, D. Davis and Y.-A. Ma, "Aiming towards the minimizers: fast convergence of SGD for overparametrized problems," preprint (arXiv:2306.02601), 2023.
  • C. Liu, A. Abedsoltan and M. Belkin, "On Emergence of Clean-Priority Learning in Early Stopped Neural Networks," preprint (arXiv:2306.02533), 2023.
  • 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, pp. e2208779120, 2023. [Link]
  • A. Abedsoltan, M. Belkin, P. Pandit and L. Rademacher, "On the Nystrom Approximation for Preconditioning in Kernel Machines," preprint (arXiv:2312.03311), 2023.
  • 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]
  • 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]
  • D. Beaglehole, M. Belkin and P. Pandit, "Kernel Ridgeless Regression is Inconsistent for Low Dimensions," preprint (arXiv:2205.13525), 2022.
  • 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]
  • 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, pp. e2115064119, 2022. [Link]
  • M. Belkin, "Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, 2021. [Link]
  • 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.
  • 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]
  • 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]
  • J. Eldridge, M. Belkin and Y. Wang, "Unperturbed: Spectral analysis beyond Davis-Kahan," Algorithmic Learning Theory, 2018. [Link]
  • 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

  • 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.
  • 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]
  • 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]
  • E. Lei, H. Hassani and S. S. Bidokhti, "Federated Neural Compression Under Heterogeneous Data," preprint (arXiv:2305.16416), 2023. (Collaboration with Hamed Hassani, Foundations team.)
  • R. Arghal, E. Lei and S. S. Bidokhti, "Robust graph neural networks via probabilistic lipschitz constraints," Learning for Dynamics and Control Conference, 2022. [Link]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • F. C. Graham, "Regularity lemmas for clustering graphs," Advances in Applied Mathematics, 2021. [Link]
  • F. C. Graham, R. Graham and S. Spiro, "Slow Fibonacci Walks," Journal of Number Theory, 2020. [Link]
  • F. C. Graham and J. Tobin, "The spectral gap of graphs arising from substring reversals," The Electronic Journal of Combinatorics, 2017. [Link]
  • 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]
  • 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

  • T. Wang, S. Herbert and S. Gao, "Fractal Landscapes in Policy Optimization," preprint (arXiv:2310.15418), 2023.
  • H. Yu, C. Hirayama, C. Yu, S. Herbert and S. Gao, "Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance," preprint (arXiv:2307.03015), 2023.
  • 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]
  • Y. Zhai and S. Gao, "Monte Carlo Tree Descent for Black-Box Optimization," Advances in Neural Information Processing Systems, 2022. [Link]
  • 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]
  • E. Y. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems," preprint (arXiv:2210.12546), 2022.
  • Y.-C. Chang, N. Roohi and S. Gao, "Neural lyapunov control," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • E. Lei, H. Hassani and S. S. Bidokhti, "Federated Neural Compression Under Heterogeneous Data," preprint (arXiv:2305.16416), 2023.
  • D. Lee, B. Moniri, X. Huang, E. Dobriban and H. Hassani, "Demystifying Disagreement-on-the-Line in High Dimensions," preprint (arXiv:2301.13371), 2023.
  • 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.
  • A. Mitra, G. J. Pappas and H. Hassani, "Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning," preprint (arXiv:2301.00944), 2023.
  • 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]
  • 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.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • 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]
  • M. Lee, O. S. Haddadin and T. Javidi, "FFT-Based Approximations for Black-Box Optimization," 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023. [Link]
  • 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]
  • S. Shekhar and T. Javidi, "Instance Dependent Regret Analysis of Kernelized Bandits," International Conference on Machine Learning, 2022. [Link]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • T. Javidi, Y. Kaspi and H. Tyagi, "Gaussian estimation under attack uncertainty," 2015 IEEE Information Theory Workshop (ITW), 2015. [Link]

Shatha Jawad

  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • B. T. Kiani, T. Le, H. Lawrence, S. Jegelka and M. Weber, "On the hardness of learning under symmetries," preprint (arXiv:2401.01869), 2024.
  • T. Le, L. Ruiz and S. Jegelka, "A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs," preprint (arXiv:2311.10610), 2023.
  • S. Gupta, J. Robinson, D. Lim, S. Villar and S. Jegelka, "Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning," preprint (arXiv:2306.13924), 2023.
  • 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]
  • 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]
  • 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.
  • 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]
  • C.-Y. Chuang, S. Jegelka and D. Alvarez-Melis, "InfoOT: Information maximizing optimal transport," International Conference on Machine Learning, 2023. [Link]
  • 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]
  • 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]
  • B. Tahmasebi and S. Jegelka, "Sample Complexity Bounds for Estimating Probability Divergences under Invariances," preprint (arXiv:2311.02868), 2023.
  • S. Jegelka, "Theory of graph neural networks: Representation and learning," preprint (arXiv:2204.07697), 2022.
  • 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]
  • 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]
  • 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%)
  • 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]
  • K. Gatmiry, S. Jegelka and J. Kelner, "Optimization and Adaptive Generalization of Three-layer Neural Networks," International Conference on Learning Representations, 2021. [Link]
  • 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]
  • V. Garg, S. Jegelka and T. Jaakkola, "Generalization and representational limits of graph neural networks," International Conference on Machine Learning, 2020. [Link]
  • 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.
  • K. Xu, W. Hu, J. Leskovec and S. Jegelka, "How powerful are graph neural networks?" preprint (arXiv:1810.00826), 2018.
  • M. Staib and S. Jegelka, "Robust budget allocation via continuous submodular functions," International Conference on Machine Learning, 2017. [Link]
  • R. Iyer, S. Jegelka and J. Bilmes, "Fast semidifferential-based submodular function optimization," International Conference on Machine Learning, 2013. [Link]

Andrew B. Kahng

  • 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," preprint (arXiv:2308.12120), 2023.
  • 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]
  • A. B. Kahng, S. Thumathy and M. Woo, "An Effective Cost-Skew Tradeoff Heuristic for VLSI Global Routing," 2023 24th International Symposium on Quality Electronic Design (ISQED), 2023. [Link]
  • 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," preprint (arXiv:2312.10589), 2023.
  • 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]
  • J. Jung, A. B. Kahng, R. Varadarajan and Z. Wang, "IEEE CEDA DATC: Expanding Research Foundations for IC Physical Design and ML-Enabled EDA," Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022. (Invited paper.) [Link]
  • 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]
  • H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. S. Sapatnekar, Z. Wang, 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]
  • 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]
  • 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]
  • A. B. Kahng, "Leveling up: A trajectory of OpenROAD, TILOS, and beyond," Proceedings of the 2022 International Symposium on Physical Design, 2022. [Link]
  • 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]
  • A. B. Kahng and Z. Wang, "ML for Design QoR Prediction," Machine Learning Applications in Electronic Design Automation, 2022. [Link]
  • 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," 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021. (Invited paper.) [Link]
  • 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," 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021. (Invited paper.) [Link]
  • 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," 2021 IEEE 39th International Conference on Computer Design (ICCD), 2021. [Link]
  • A. B. Kahng, "Machine learning applications in physical design: Recent results and directions," Proceedings of the 2018 International Symposium on Physical Design, 2018. [Link]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • W. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular maximization in clean linear time," Advances in Neural Information Processing Systems, 2022. [Link]
  • S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal rates for interactive learning," Advances in Neural Information Processing Systems, 2022. [Link]
  • 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]
  • 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.
  • 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]
  • I. Han, M. Gartrell, E. Dohmatob and A. Karbasi, "Scalable MCMC sampling for nonsymmetric determinantal point processes," International Conference on Machine Learning, 2022. [Link]
  • 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]
  • 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.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • S. Hussain, T. Huster, C. Mesterharm, P. Neekhara and F. Koushanfar, "ReFace: Adversarial Transformation Networks for Real-time Attacks on Face Recognition Systems," 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2023. [Link]
  • 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]
  • R. Zhang, M. Javaheripi, Z. Ghodsi, A. Bleiweiss and F. Koushanfar, "AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs," 2023 60th ACM/IEEE Design Automation Conference (DAC), 2023. [Link]
  • 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]
  • 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. [Link]
  • 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]
  • 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]
  • 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.
  • 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]
  • 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]
  • 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]
  • 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

  • Y. Tao, X. Liu, I. Spasojevic, S. Agarwal and V. Kumar, "3D Active Metric-Semantic SLAM," IEEE Robotics and Automation Letters, 2024. [Link]
  • 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.
  • 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]
  • 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]
  • L. Zhou and V. Kumar, "Robust multi-robot active target tracking against sensing and communication attacks," IEEE Transactions on Robotics, 2023. [Link]
  • 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]
  • 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]
  • I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," preprint (arXiv:2305.18193), 2023.
  • 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," preprint (arXiv:2308.06658), 2023.
  • I. Boero, I. Spasojevic, M. d. Castillo, G. Pappas, V. Kumar and A. Ribeiro, "Navigation with shadow prices to optimize multi-commodity flow rates," preprint (arXiv:2309.14284), 2023.
  • 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.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • M. Quigley, K. Mohta, S. S. Shivakumar, M. Watterson, Y. Mulgaonkar, M. Arguedas, K. Sun, S. Liu, B. Pfrommer, V. Kumar, 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M. E. Khan and M. Schmidt, "Simplifying Momentum-based Riemannian Submanifold Optimization," preprint (arXiv:2302.09738), 2023.
  • 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]
  • 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]
  • V. Duruisseaux and M. Leok, "Practical perspectives on symplectic accelerated optimization," Optimization Methods and Software, 2023. [Link]
  • X. Shen and M. Leok, "Geometric exponential integrators," Journal of Computational Physics, 2019. [Link]
  • M. Leok, "Variational discretizations of gauge field theories using group-equivariant interpolation," Foundations of Computational Mathematics, 2019. [Link]
  • J. Hall and M. Leok, "Lie group spectral variational integrators," Foundations of Computational Mathematics, 2017. [Link]
  • H. Parks and M. Leok, "Variational integrators for interconnected Lagrange-Dirac systems," Journal of Nonlinear Science, 2017. [Link]
  • 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

  • Y. Lin, Y. Ma, Y.-X. Wang and R. Redberg, "Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy," preprint (arXiv:2310.14661), 2023.
  • N. L. Kuang, M. Yin, M. Wang, Y.-X. Wang and Y.-A. Ma, "Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation," preprint (arXiv:2310.18919), 2023.
  • 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.
  • K. Kim, Y. Ma and J. R. Gardner, "Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?" preprint (arXiv:2307.14642), 2023.
  • 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]
  • 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]
  • D. Wu, R. Niu, M. Chinazzi, Y. Ma and R. Yu, "Disentangled Multi-Fidelity Deep Bayesian Active Learning," preprint (arXiv:2305.04392), 2023.
  • 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]
  • C. Liu, D. Drusvyatskiy, M. Belkin, D. Davis and Y.-A. Ma, "Aiming towards the minimizers: fast convergence of SGD for overparametrized problems," preprint (arXiv:2306.02601), 2023.
  • 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]
  • A. Roy, G. So and Y.-A. Ma, "Optimization on Pareto sets: On a theory of multi-objective optimization," preprint (arXiv:2308.02145), 2023.
  • 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]
  • 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]
  • R. Shen, L. Gao and Y.-A. Ma, "On Optimal Early Stopping: Over-informative versus Under-informative Parametrization," preprint (arXiv:2202.09885), 2022.
  • 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]
  • 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]
  • 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]
  • E. Mazumdar, A. Pacchiano, Y. Ma, P. L. Bartlett and M. I. Jordan, "On Thompson Sampling with Langevin Algorithms," preprint (arXiv:2002.10002), 2020.
  • 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]
  • 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

  • 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]
  • 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]
  • A. Ghosh, A. Mazumdar, A. Mazumdar, et al., "An Improved Algorithm for Clustered Federated Learning," preprint (arXiv:2210.11538), 2022.
  • 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]
  • 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]
  • V. Gandikota, A. Mazumdar and S. Pal, "Recovery of sparse linear classifiers from mixture of responses," Advances in Neural Information Processing Systems, 2020. [Link]
  • S. Pal and A. Mazumdar, "Recovery of sparse signals from a mixture of linear samples," International Conference on Machine Learning, 2020. [Link]
  • 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.
  • 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]
  • 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

  • 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]
  • 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]
  • 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," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022. [Link]
  • 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]
  • 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," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022. [Link]
  • K. Zhu, H. Chen, M. Liu and D. Z. Pan, "Automating analog constraint extraction: From heuristics to learning," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022. (Invited paper.) [Link]
  • 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," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022. (Invited paper.) [Link]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • 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

  • J. Cerviño, L. Ruiz and A. Ribeiro, "Learning by Transference: Training Graph Neural Networks on Growing Graphs," IEEE Transactions on Signal Processing, 2023. [Link]
  • 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]
  • I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," preprint (arXiv:2305.18193), 2023.
  • 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," preprint (arXiv:2308.06658), 2023.
  • 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]
  • 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]
  • I. Hounie, A. Ribeiro and L. F. O. Chamon, "Resilient Constrained Learning," preprint (arXiv:2306.02426), 2023.
  • I. Boero, I. Spasojevic, M. d. Castillo, G. Pappas, V. Kumar and A. Ribeiro, "Navigation with shadow prices to optimize multi-commodity flow rates," preprint (arXiv:2309.14284), 2023.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • S. Segarra, A. G. Marques and A. Ribeiro, "Distributed linear network operators using graph filters," preprint (arXiv:1510.03947), 2015.
  • 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

  • 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]
  • 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," 2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2023. [Link]
  • 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]
  • W. Xu, V. Swaminathan, S. Pinge, S. Fuhrman and T. Rosing, "HyperMetric: Robust Hyperdimensional Computing on Error-prone Memories using Metric Learning," 2023 IEEE 41st International Conference on Computer Design (ICCD), 2023. [Link]
  • 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]
  • Q. Zhao, A. Thomas, A. Brin, X. Yu and T. Rosing, "Unleashing Hyperdimensional Computing with Nyström Method based Encoding," preprint, 2023. [Link]
  • 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.
  • M. Timken, O. Gungor, T. Rosing and B. Aksanli, "Analysis of Machine Learning Algorithms for Cyber Attack Detection in SCADA Power Systems," 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), 2023. [Link]
  • 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]
  • I. G. Moreno, X. Yu and T. Rosing, "KalmanHD: Robust On-Device Time Series Forecasting with Hyperdimensional Computing," preprint, 2023. [Link]
  • D. Liu, K. Ergun and T. Š. Rosing, "Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification," 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. [Link]
  • 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]
  • 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

  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • K. Ahn, X. Cheng, M. Song, C. Yun, A. Jadbabaie and S. Sra, "Linear attention is (maybe) all you need (to understand transformer optimization)," preprint (arXiv:2310.01082), 2023.
  • K. Ahn, X. Cheng, H. Daneshmand and S. Sra, "Transformers learn to implement preconditioned gradient descent for in-context learning," preprint (arXiv:2306.00297), 2023.
  • D. X. Wu, C. Yun and S. Sra, "On the Training Instability of Shuffling SGD with Batch Normalization," International Conference on Machine Learning, 2023. (Work with David Wu, co-supervised by the other authors.) [Link]
  • X. Cheng, J. Zhang and S. Sra, "Theory and Algorithms for Diffusion Processes on Riemannian Manifolds," preprint (arXiv:2204.13665), 2022.
  • X. Cheng, J. Zhang and S. Sra, "Efficient Sampling on Riemannian Manifolds via Langevin MCMC," Advances in Neural Information Processing Systems, 2022. [Link]
  • 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]
  • M. Weber and S. Sra, "On a class of geodesically convex optimization problems solved via Euclidean MM methods," preprint (arXiv:2206.11426), 2022.
  • M. Weber and S. Sra, "Computing Brascamp-Lieb Constants through the lens of Thompson Geometry," preprint (arXiv:2208.05013), 2022.
  • J. Jin and S. Sra, "Understanding Riemannian acceleration via a proximal extragradient framework," Conference on Learning Theory, 2022. [Link]
  • 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%)
  • S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," preprint (arXiv:2112.00056), 2021.
  • 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]
  • C. Yun, S. Rajput and S. Sra, "Minibatch vs. local SGD with shuffling: Tight convergence bounds and beyond," preprint (arXiv:2110.10342), 2021.
  • 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.)
  • K. Ahn and S. Sra, "From Nesterov’s estimate sequence to Riemannian acceleration," Conference on Learning Theory, 2020. [Link]
  • 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]
  • 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]
  • H. Zhang and S. Sra, "First-order methods for geodesically convex optimization," Conference on Learning Theory, 2016. [Link]
  • 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

  • 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]
  • 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.
  • J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, H. Su, Y. Tang, S. Tao, X. Wei, Y. Yao, Y. Yao, et al., "ManiSkill2: A unified benchmark for generalizable manipulation skills," preprint (arXiv:2302.04659), 2023.
  • 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.
  • 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]
  • 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]
  • 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.
  • 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.
  • N. Hansen, X. Wang and H. Su, "Temporal difference learning for model predictive control," preprint (arXiv:2203.04955), 2022.
  • J. Gu, D. S. Chaplot, H. Su and J. Malik, "Multi-skill mobile manipulation for object rearrangement," preprint (arXiv:2209.02778), 2022. (Spotlight.)
  • 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]
  • Z. Jia and H. Su, "Information-theoretic local minima characterization and regularization," International Conference on Machine Learning, 2020. [Link]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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.
  • 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]
  • 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, 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • O. Mangoubi and N. K. Vishnoi, "Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations," preprint (arXiv:2306.16648), 2023.
  • 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," preprint (arXiv:2306.09835), 2023.
  • L. E. Celis, A. Kumar, A. Mehrotra and N. K. Vishnoi, "Bias in Evaluation Processes: An Optimization-Based Model," preprint (arXiv:2310.17489), 2023.
  • 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]
  • O. Mangoubi and N. Vishnoi, "Sampling from log-concave distributions with infinity-distance guarantees," Advances in Neural Information Processing Systems, 2022. [Link]
  • 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]
  • O. Mangoubi and N. K. Vishnoi, "Faster Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk," preprint (arXiv:2206.09384), 2022.
  • 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]
  • A. Mehrotra and N. Vishnoi, "Fair ranking with noisy protected attributes," Advances in Neural Information Processing Systems, 2022. [Link]
  • V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A convergent and dimension-independent min-max optimization algorithm," preprint (arXiv:2006.12376), 2020.
  • 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]
  • O. Mangoubi and N. K. Vishnoi, "Nonconvex sampling with the Metropolis-adjusted Langevin algorithm," Conference on Learning Theory, 2019. [Link]
  • 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]
  • H. Lee, O. Mangoubi and N. Vishnoi, "Online sampling from log-concave distributions," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  • 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

  • Z.-H. Yin, B. Huang, Y. Qin, Q. Chen and X. Wang, "Rotating without Seeing: Towards In-hand Dexterity through Touch," preprint (arXiv:2303.10880), 2023.
  • Y.-H. Wu, J. Wang and X. Wang, "Learning generalizable dexterous manipulation from human grasp affordance," Conference on Robot Learning, 2023. [Link]
  • 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]
  • Y. Lin, Y. Ma, Y.-X. Wang and R. Redberg, "Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy," preprint (arXiv:2310.14661), 2023.
  • 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]
  • N. L. Kuang, M. Yin, M. Wang, Y.-X. Wang and Y.-A. Ma, "Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation," preprint (arXiv:2310.18919), 2023.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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.
  • 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.
  • N. Hansen, X. Wang and H. Su, "Temporal difference learning for model predictive control," preprint (arXiv:2203.04955), 2022.
  • 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," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. [Link]
  • 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]
  • R. Yang, M. Zhang, N. Hansen, H. Xu and X. Wang, "Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers," preprint (arXiv:2107.03996), 2021.
  • 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]
  • 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]
  • R. Yang, H. Xu, Y. Wu and X. Wang, "Multi-task reinforcement learning with soft modularization," Advances in Neural Information Processing Systems, 2020. [Link]
  • 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.
  • 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]
  • X. Wang and A. Gupta, "Videos as space-time region graphs," Proceedings of the European Conference on Computer Vision (ECCV), 2018. [Link]
  • W. Yang, X. Wang, A. Farhadi, A. Gupta and R. Mottaghi, "Visual semantic navigation using scene priors," preprint (arXiv:1810.06543), 2018.

Yusu Wang

  • S. Chen and Y. Wang, "Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions," preprint (arXiv:2308.00273), 2023.
  • 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]
  • G. Mishne, Z. Wan, Y. Wang and S. Yang, "The numerical stability of hyperbolic representation learning," International Conference on Machine Learning, 2023. [Link]
  • 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]
  • C. Cai, T. S. Hy, R. Yu and Y. Wang, "On the connection between MPNN and graph transformer," preprint (arXiv:2301.11956), 2023.
  • 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.
  • 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," preprint (arXiv:2312.10589), 2023.
  • A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," preprint (arXiv:2302.03771), 2023.
  • S. Chen, S. Lim, F. Mémoli, Z. Wan and Y. Wang, "Weisfeiler-Lehman meets Gromov-Wasserstein," International Conference on Machine Learning, 2022. [Link]
  • 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]
  • C. Cai and Y. Wang, "Convergence of invariant graph networks," International Conference on Machine Learning, 2022. [Link]
  • 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]
  • 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]
  • T. K. Dey, J. Wang and Y. Wang, "Graph reconstruction by discrete Morse theory," preprint (arXiv:1803.05093), 2018.
  • J. Eldridge, M. Belkin and Y. Wang, "Unperturbed: Spectral analysis beyond Davis-Kahan," Algorithmic Learning Theory, 2018. [Link]
  • A. Sidiropoulos, D. Wang and Y. Wang, "Metric embeddings with outliers," Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms, 2017. [Link]
  • J. Eldridge, M. Belkin and Y. Wang, "Graphons, mergeons, and so on!" Advances in Neural Information Processing Systems, vol. 29, 2016. [Link]