TILOS PUBLICATIONS

Publications that acknowledge TILOS (NSF CCF-2112665) support

 

Education and Workforce Development

  1. 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”, American Society for Engineering Education (ASEE) Conference, June 2023.
  2. R. P. Uhlig, S. J. 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”, American Society for Engineering Education (ASEE) Conference, June 2023.

Foundations

  1. D. Beaglehole, M. Belkin and P. Pandit, “Kernel Ridgeless Regression is Inconsistent for Low Dimensions”, Proc. SIMODS, 2023.
  2. 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).
  3. M. Black, A. Nayyeri, Z. Wan and Y. Wang, “Understanding Oversquashing in GNNs through the Lens of Effective Resistance”, 40th Intl. Conf. Machine Learning (ICML), to appear, 2023.
  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”, ICML, to appear 2023.
  5. C. Cai, T. S. Hy, R. Yu and Y. Wang, “On the connection between MPNN and Graph Transformer”, Proc. ICML, to appear 2023.
  6. C. Cai and Y. Wang, “Convergence of Invariant Graph Networks,” Intl. Conf. on Machine Learning (ICML), 2022.
  7. Y. Cao, Z. Chen, M. Belkin and Q. Gu, “Benign overfitting in two-layer convolutional neural networks”, Proc. NeurIPS, 2022, pp. 25237-25250.
  8. N. Chandramoorthy, A. Loukas, K. Gatmiry and S. Jegelka, “On the generalization of learning algorithms that do not converge,” NeurIPS, 2022.
  9. S. Chen, S. Lim, F. Memoli, Z. Wan and Y. Wang, “Weisfeiler-Lehman Meets Gromov-Wasserstein”, International Conference on Machine Learning (ICML), 2022.
  10. X. Cheng, J. Zhang, and S. Sra, “Theory and Algorithms for Diffusion Processes on Riemannian Manifolds”, arXiv preprint arXiv:2204.13665. (Link)
  11. X. Cheng, J. Zhang, and S. Sra, “Efficient Sampling on Riemannian Manifolds via Langevin MCMC,” NeurIPS, 2022.
  12. Y. Zhai and S. Gao, "Monte Carlo Tree Descent for Black-Box Optimization", NeurIPS, 2022.
  13. C-Y. Chuang and S. Jegelka, “Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks”, NeurIPS, 2022.
  14. C-Y. Chuang, S. Jegelka and D. Alvarez-Melis, “InfoOT: Information Maximizing Optimal Transport”, Proc. ICML, 2023.
  15. J. Dadashkarimi, A. Karbasi and D. Scheinost, "Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport", Proc. MICCAI, 2022.
  16. V. Duruisseaux and M. Leok, “Practical Perspectives on Symplectic Accelerated Optimization”, Optimization Methods and Software, accepted, 2023.
  17. Y. Freund, Y.-A. Ma and T. Zhang, “When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint”, Journal of Machine Learning Research (JMLR), 23 (214): 1−32, 2022.
  18. A. Guelen, F. Memoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps", Intl. Symp. Comput. Geom. (SoCG), to appear, 2023.
  19. I. Han, M. Gartrell, E. Dohmatob and A. Karbasi, "Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes", Proc. ICML, 2022.
  20. I. Han, A. Zandieh, J. Lee, R. Novak, L. Xiao and A. Karbasi, "Fast Neural Kernel Embeddings for General Activations", NeurIPS, 2022.
  21. I. Mehalel, S. Hanneke, S. Moran, M. Mahmoody and A. Karbasi, "On Optimal Learning Under Targeted Data Poisoning", NeurIPS, 2022.
  22. E. Y. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems", NeurIPS, 2022.
  23. Harshvardhan, A. Ghosh and A. Mazumdar, “An Improved Algorithm for Clustered Federated Learning”, submitted to NeurIPS 2023. https://arxiv.org/abs/2210.11538
  24. L. Hui, M. Belkin and S. Wright, “Cut your Losses with Squentropy”, Proc. ICLR, 2023
  25. A. Jadbabaie, H. Mania, D. Shah and S. Sra, "Time varying regression with hidden linear dynamics," accepted to appear in 4th Annual Learning for Dynamics & Control Conference (L4DC), 2022. arXiv:2112.14862. (Work with Postdoc Horia Mania co-supervised by the other named authors.)
  26. J. Jin and S. Sra, “Understanding Riemannian Acceleration via a Proximal Extragradient Framework,” Proc. Conference on Learning Theory, July 2022, pp. 2924-2962.
  27. A. Kalavasis, G. Velegkas and A. Karbasi, "Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes", NeurIPS, 2022.
  28. A. Karalias, J. Robinson, A. Loukas and S. Jegelka, “Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions,” NeurIPS, 2022.
  29. S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal Rates for Interactive Learning", NeurIPS, 2022.
  30. A. Karbasi, N. Kuang, Y.-A. Ma and S. Mitra, “Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning”, Proc. ICML, to appear 2023. (Collaborative with Amin Karbasi and Yian Ma)
  31. V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent First-Order Algorithm for Min-Max Optimization," axXiv preprint, 2021.  Intl. Conf. on Machine Learning, 2022. (Link)
  32. D. Lee, B. Moniri, X. Huang, E. Dobriban and H. Hassani, “Demystifying Disagreementon-the-Line in High Dimensions”, Proc. ICML, 2023, to appear. https://arxiv.org/pdf/2301.13371.pdf
  33. B. Li, Y.-A. Ma, J. N. Kutz and X. Yang, "The Adaptive Spectral Koopman Method for Dynamical Systems", SIAM Journal on Applied Dynamical Systems, (to appear).
  34. B. Li, Y. Ma, J. Kutz, and X. Yang, “The Adaptive Spectral Koopman Method for Dynamical Systems,” arXiv preprint arXiv:2202.09501, 2022. (Link)
  35. B. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular Maximization in Clean Linear Time", NeurIPS, 2022.
  36. D. Lim, J. D. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron and S. Jegelka, "Sign and Basis Invariant Networks for Spectral Graph Representation Learning" Intl. Conf. on Learning Representations (ICLR), 2023. (Spotlight/notable-top-25%)
  37. W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M.E. Khan, M. Schmidt, “Simplifying Momentum-based Riemannian Submanifold Optimization”, International Conference on Machine Learning (ICML), accepted, 2023.
  38. N. Mallinar, J. Simon, A. Abedsoltan, P. Pandit, M. Belkin and P. Nakkiran, “Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting”, Proc. NeurIPS, 2022, pp. 1182-1195.
  39. O. Mangoubi and N. K. Vishnoi, “Sampling from log-concave distributions with infinity-distance guarantees,” NeurIPS, 2022. (Link)
  40. O. Mangoubi, N. K. Vishnoi, "Faster Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk," arxiv, 2022. (Link)
  41. O. Mangoubi and N. K. Vishnoi, “Re-Analyze Gauss: Bounds for private matrix approximation via Dyson Brownian motion,” Annual Conf. on Neural Information Processing Systems (NeurIPS), 2022. (Link).
  42. A. Mehrotra, B. S. Pradelski and N. K. Vishnoi, "Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints," ACM Conf. on Fairness, Accountability, and Transparency, June, 2022. (Link)) [Also:  Computing Research Repository (CoRR), February 2022. (Link)]
  43. A. Mehrotra and N. K. Vishnoi, “Fair ranking with noisy protected attributes,” NeurIPS, 2022. (Link)
  44. A. Mehrotra and N. K. Vishnoi, “Maximizing Submodular Functions for Recommendation in the Presence of Biases”, The Web Conference WWW, 2023, pp. 3625-3636. https://arxiv.org/abs/2305.02806
  45. F. Memoli, Z. Wan and Y. Wang, “Persistent Laplacians: properties, algorithms and implications”, SIAM Journal on Mathematics of Data Science (SIMODS), 2022.
  46. G. Mishne, Z. Wang, Y. Wang and S. Yang, “The numerical stability of hyperbolic representation learning”, Proc. ICML, to appear 2023.
  47. A. Mitra, G. J. Pappas and H. Hassani, “Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning”, in submission. https://arxiv.org/pdf/2301.00944.pdf
  48. K. E. Nikolakakis, F. Haddadpour, D. S. Kalogerias, A. Karbasi, "Black-Box Generalization", NeurIPS, 2022.
  49. A. Radhakrishnan, M. Belkin and C. Uhler, “Wide and deep neural networks achieve consistency for classification”, Proceedings of the National Academy of Sciences, 120(14), e2208779120.
  50. A. Radhakrishnan, G. Stefanakis, M. Belkin and C. Uhler, “Simple, fast, and flexible framework for matrix completion with infinite width neural networks”, Proc. National Academy of Sciences, 119(16), e2115064119. https://doi.org/10.1073/pnas.2115064119
  51. Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Self-Consistency of the Fokker-Planck Equation", Proc. COLT, 2022.
  52. S. Sra and M. Weber, “On a class of geodesically convex optimization problems solved via Euclidean MM methods,” arXiv preprint arXiv:2206.11426, (link).
  53. Tahmasebi, D. Lim and S. Jegelka, "The Power of Recursion in Graph Neural Networks for Counting Substructures", Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), 2023. (Oral Presentation) (Top 1.9% of submissions)
  54. Y. Tian, K. Zhang, R. Tedrake and S. Sra, “Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?” accepted at L4DC 2023 (Oral presentation).
  55. G. Velegkas, Z. Yang and A. Karbasi, "The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches", NeurIPS, 2022.
  56. M. Weber and S. Sra, “Computing Brascamp-Lieb Constants through the lens of Thompson Geometry,” arXiv preprint arXiv:2208.05013, (link).
  57. D. Wu, M. Chinazzi, A. Vespignani, Y.-A. Ma and R. Yu, "Multi-fidelity Hierarchical Neural Processes", KDD, 2022.
  58. D. Wu, R. Niu, M. Chinazzi, Y.-A. Ma and R. Yu, “Disentangled Multi-Fidelity Deep Bayesian Active Learning”, Proc. ICML, to appear 2023.
  59. D. X. Wu, C. Yun and S. Sra, “On the Training Instability of Shuffling SGD with Batch Normalization,” Proc. ICML, to appear 2023. (Work with David Wu, co-supervised by the other authors.)
  60. X. Xia, G. Mishne, and Y. Wang, "Implicit Graphon Neural Representation", Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), to appear, 2023. (Oral Presentation) (Top 1.9% of submissions)
  61. P. H. Zadeh and S. Sra, "Introducing Discrepancy Values of Matrices with Application to Bounding Norms of Commutators," Linear Algebra and its Applications, 25 pages. (Work with PhD student Pourya Zadeh supervised by Sra.)
  62. H. Zhu, A. Ghosh and A. Mazumdar, “Optimal Compression of Unit Norm Vectors in the High Distortion Regime”, IEEE International Symposium on Information Theory (ISIT), 2023.
  63. Y. Freund, Y.-A. Ma and T. Zhang, "When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint," (preprint 2021, will acknowledge NSF CCF-2112665 in publication).
  64. K. Gatmiry, S. Jegelka, J. Kelner, "Optimization and Adaptive Generalization of Three layer Neural Networks," Intl. Conf. on Learning Representations, 2022. (Link)
  65. H. Hassani and A. Javanmard, “The curse of overparameterization in adversarial training: Precise analysis of robust generalization for random features regression,” arXiv preprint arXiv:2201.05149, 2022. (Link)
  66. A. Jadbabaie, H. Mania, D. Shah and Suvrit Sra, "Time varying regression with hidden linear dynamics," arXiv:2112.14862. (Preprint) (Link)
  67. S. Jegelka, "Theory of Graph Neural Networks: Representation and Learning," Proc. of the Intl. Congress of Mathematicians (ICM), 2022.
  68. V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent Min-Max Optimization Algorithm," Proc. of Machine Learning Research, 2022. (Link)
  69. O. Mangoubi and N. K. Vishnoi, "Sampling from Log-Concave Distributions with Infinity-Distance Guarantees," arXiv preprint,2022. (Link)
  70. E. McCarty, Q. Zhao, A. Sidiropoulos and Y. Wang, "NN-Baker: A neural-network infused algorithmic framework for optimization problems on geometric intersection graphs," Conf. on Neural Information Processing Systems, 2021, to appear.
  71. J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka and S. Sra, "Can contrastive learning avoid shortcut solutions?" Conf. on Neural Information Processing Systems, 2021.
  72. R. Shen, L. Gao and Y. Ma, “On Optimal Early Stopping: Over-informative versus Under-informative Parametrization,” arXiv preprint arXiv:2202.09885, 2022. (Link)
  73. S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," arXiv:2112.00056. (Preprint) (Link)
  74. B. Tahmasebi, D. Lim, S. Jegelka, "The Power of Recursion in Graph Neural Networks for Counting Substructures," ICML workshop on Topology, Algebra and Geometry in Data Science, 2022.
  75. C. Yun, S. Rajput and S. Sra, "Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond," Intl. Conf. on Learning Representations, 2022. (Link)
  76. 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)
  77. W. Mou, Y.-A. Ma, M. J. Wainwright, P. L. Bartlett and M. I. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm," Journal of Machine Learning Research (JMLR), 2021. (Link)
  78. V. Gandikota, A. Mazumdar and S. Pal, "Support Recovery of Sparse Signals from a Mixture of Linear Measurements," Conf. on Neural Information Processing Systems, 2021.
  79. 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, 2021, pp. 942-953.
  80. M. Belkin, "Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, Cambridge University Press, 2021, pp. 203-248. (Link)

Chips

  1. A. F. Budak, D. Smart, B. Swahn and D. Z. Pan, “APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors using DNN Learning,” Proc. IEEE/ACM Asia and South Pacific Design Automation Conference(ASPDAC), January 2023.
  2. I. Bustany, A. B. Kahng, Y. Koutis, B. Pramanik and Z. Wang, "SpecPart: A Supervised Spectral Framework for Hypergraph Partitioning Solution Improvement", (.pdf), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022 (Best Paper Award).
  3. C-K. Cheng, A. B. Kahng, S. Kundu, Y. Wang and Z. Wang, "Assessment of Reinforcement Learning for Macro Placement", (.pdf), Proc. ACM/IEEE Intl. Symp. on Physical Design, 2023, to appear. (Invited Paper) [arXiv]
  4. V. A. Chhabria, W. Jiang, A. B. Kahng, S. S. Sapatnekar, "From Global Route to Detailed Route: ML for Fast and Accurate Wire Parasitics and Timing Prediction", (.pdf), Proc. ACM/IEEE Workshop on Machine Learning for CAD, 2022.
  5. H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. S. Sapatnekar, Z. Wang and Z. Zeng, "Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms", (.pdf), Proc. ACM/IEEE Workshop on Machine Learning for CAD, 2022.
  6. Z. Jiang, M. Liu, Z. Guo, S. Zhang, Y. Lin, and D. Z. Pan, “A Tale of EDA’s Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation,” ACM/IEEE Workshop on Machine Learning for CAD, 2022.
  7. J. Jung, A. B. Kahng, R. Varadarajan and Z. Wang, “IEEE CEDA DATC: Expanding Research Foundations for IC Physical Design and ML-Enabled EDA,” (.pdf), (.pptx), (.mp4), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022. (Invited Paper).
  8. A.B. Kahng, “A Mixed Open-Source and Proprietary EDA Commons for Education and Prototyping,”, (.pdf), (.pptx), (.mp4), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022. (Invited Paper).
  9. A.B. Kahng, "Machine Learning for CAD/EDA: The Road Ahead," IEEE Design and Test (Special Issue on Machine Learning for CAD/EDA), January 2023.
  10. A.B. Kahng, S. Thumathy and M. Woo, “An Effective Cost-Skew Tradeoff Heuristic for VLSI Global Routing,” accepted to appear in Proc. International Symposium on Quality Electronic Design, 2023.
  11. A.B. Kahng and Z. Wang, “ML for Design QoR Prediction,” chapter in Machine Learning Applications in Electronic Design Automation (H. Ren and J. Hu, eds.), Springer. Published January 8, 2023.
  12. H. Zhu, H. Chen, W. Turner, G. Kokai, P. Wei, D. Z. Pan, and H. Ren, “TAG: Learning Circuit Spatial Embedding From Layouts,” Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022.
  13. A.F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini and D. Z. Pan, “Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives,” IEEE/ACM Asia and South Pacific Design Automation Conf. (ASP-DAC), January, 2022. (Invited Paper)
  14. 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 (TECS), 2022.
  15. 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,” Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design, 2021, pp. 1-6. (Invited Paper)
  16. 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," Proc. ACM/IEEE Intl. Conf. on Computer Design, 2021, pp. 366-373. (Link)
  17. J. Jung, A. B. Kahng, S. Kim and R. Varadarajan, "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD," Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design, 2021, pp. 1-9. (Invited Paper) (Link)
  18. A.B. Kahng, R. Varadarajan and Z. Wang, “RTL-MP: Toward Practical, Human-Quality Chip Planning and Macro Placement,” Proc. ACM/IEEE Intl. Symp. on Physical Design, 2022. (Link)
  19. 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," IEEE/ACM Asian and South Pacific Design Automation Conf. (ASP-DAC), January 2022.
  20. 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," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021. (Keynote Paper)
  21. K. Zhu, H. Chen, M. Liu and D. Z. Pan, “Automating Analog Constraint Extraction: From Heuristics to Learning,” IEEE/ACM Asia and South Pacific Design Automation Conf. (ASP-DAC), January, 2022. (Invited Paper)
  22. 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," IEEE/ACM Asian and South Pacific Design Automation Conf. (ASP-DAC), January 2022.
  23. A.B. Kahng, "Leveling Up: A Trajectory of OpenROAD, TILOS and Beyond," Proc. ACM/IEEE Intl. Symp. on Physical Design, 2022, pp. 73-79. (Link)
  24. 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,” Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design, 2021, pp. 1-6. (Invited Paper)
  25. H.Chen, C. Fu, J. Zhao and F. Koushanfar, "GALU: A Genetic Algorithm Framework for Logic Unlocking," Digital Threats: Research and Practice, 2021.
  26. Y.Lin, Z. Jiang, J. Gu, W. Li, S. Dhar, H. Ren, B. Khailany and D. Z. Pan, "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021, pp. 748-761. (Link)

Networks

  1. R. Arghal, E. Lei, S. Saeedi Bidokhti, “Robust Graph Neural Networks via Probabilistic Lipschitz Constraints," Proc. Conf. on Learning for Dynamics and Control (L4DC), 2022.
  2. J. Cervino, L. Chamon, B. D Haeffele, R. Vidal and A. Ribeiro, “Learning Globally Smooth Functions on Manifolds”, in submission.
  3. J. Cervino, L. Ruiz and A. Ribeiro, “Learning by Transference: Training Graph Neural Networks on Growing Graphs”, IEEE Transactions on Signal Processing, 2023.
  4. X. Chen, H. Nikpey, J. Kim, S. Sarkar, S. Saeedi Bidokhti, “Containing a spread through sequential learning: to exploit or to explore?” Transactions on Machine Learning Research (TMLR), to appear.
  5. E. Ekaireb, X. Yu, K. Ergun, Q. Zhao, K. Lee, M. Huzaifa, and T. Rosing, “ns3-fl: Simulating Federated Learning with ns-3,” Workshop of NS3 (WNS3), 2022.
  6. M. Lee, S. Shekar and T. Javidi, “Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS,” Proc. of IEEE Int. Symp. on Information Theory (ISIT), 2022.
  7. E. Lei, H. Hassani, S. Saeedi Bidokhti, “On a Relation Between the Rate-Distortion Function and Optimal Transport," ICML Tiny papers, 2023, to appear (collaboration with Hamed Hassani, foundations team).
  8. E. Lei, H. Hassani, S. Saeedi Bidokhti, “Federated Neural Compression,” Proc. ISIT, 2023, to appear (collaboration with Hamed Hassani, foundations team).
  9. E. Lei, H. Hassani and S. S. Diokhti, “Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding,” Proc. of IEEE Int. Symp. on Information Theory (ISIT), 2022.
  10. E. Lei, H. Hassani, S. Saeedi Bidokhti, “Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding," J. Selected Areas in Inf. Theory, to appear, 2023 (collaboration with Hamed Hassani, foundations team).
  11. S. Shekar and T. Javidi, “Instance-dependent Regret Analysis of Kernelized Bandits,” Intl. Conf. on Machine Learning (ICML), 2022.
  12. X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-Peer Variational Federated Learning over Arbitrary Graphs," to appear in IEEE Journal of Selected Areas in Information Theory .
  13. 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", ACM/IEEE Conf. on Internet of Things Design and Implementation (IoTDI), 2023.
  14. Q. Zhao, K. Lee, J. Liu, M. Huzaifa, X. Yu and T. Rosing, “FedHD: Federated Learning with Hyperdimensional Computing”, ACM Annual International Conference on Mobile Computing and Networking (MobiCom Demo), 2022.
  15. Q. Zhao, X. Yu and T. Rosing, “Poster Abstract: Attentive Multimodal Learning on Sensor Data using Hyperdimensional Computing", ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN Poster), 2023.
  16. V. Kungurtsev, M. Morafah, T. Javidi and G. Scutari. “Decentralized Asynchronous Nonconvex Stochastic Optimization on Directed Graphs”, to appear in IEEE Transactions on Control of Network Systems, 2023.
  17. V. Kungurtsev, A. Cobb, T. Javidi and B. Jalaian. “Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo”, Machine Learning (2023). https://doi.org/10.1007/s10994-023-06345-6
  18. X. Zheng, T. Javidi and B. Touri. “Zeroth-Order Non-Convex Optimization for Cooperative Multi-Agent Systems with Diminishing Step Size and Smoothing Radius.” to appear in IEEE Control Systems Letters (L-CSS), 2023.
  19. M. Lee, O. S. Haddadin and T. Javidi. “FFT-Based Approximations for Black-Box Optimization”, Proceedings of the IEEE Statistical Signal Processing Workshop (SSP), July 2023.

Robotics

  1. V. Duruisseaux, T. Duong, M. Leok and N. Atanasov, “Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems,” PMLR Learning for Dynamics and Control Conference (L4DC), 2023.
  2. J. Gu, D. S. Chaplot, H. Su and J. Malik, “Multi-skill Mobile Manipulation for Object Rearrangement”, Proc. ICLR, 2023 (Spotlight).
  3. J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, Y. Tang, S. Tao, X. Wei, Y. Yao, X. Yuan, P. Xie, Z. Huang, R. Chen and H. Su, “ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills”, Proc. ICLR, 2023.
  4. N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar and A. Rajeswaran, “MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations”, Proc. ICLR, 2023.
  5. N. Hansen, X. Wang and H. Su, “Temporal Difference Learning for Model Predictive Control”, Proc. ICML, 2022
  6. N. Hansen, Z. Yuan, Y. Ze, T. Mu, A. Rajeswaran, H. Su, H. Xu and X. Wang, “On PreTraining for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline”, Proc. ICML, 2023.
  7. C. S. Imai, M. Zhang, Y. Zhang, M. Kierebiński, R. Yang, Y. Qin and X. Wang, “VisionGuided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization”, Proc. IROS 2022.
  8. S. Kumar, J. Zamora, N. Hansen, R. Jangir and X. Wang, “Graph Inverse Reinforcement Learning from Diverse Videos”, Conf. on Robot Learning (CoRL), 2022 (Oral Presentation).
  9. 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”, Proc. ICLR, 2023.
  10. 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,” Intl. Conf. on Robotics and Automation, 2023, to appear.
  11. K. Mao, J. Welde, A. Hsieh and V. Kumar, “Trajectory Planning for the Bidirectional Quadrotor as a Differentially Flat Hybrid System,” International Conference on Robotics and Automation, to appear 2023.
  12. R. Patil, A.r Langley, H. I. Christensen, “Scaling up multi-agent patrolling in urban environments”, SPIE Defense and Commercial Sensing, 2023.
  13. Y. Qin, B. Huang, Z. Yin, H. Su and X. Wang, “Generalizable Point Cloud Policy Learning for Sim-to-Real Dexterous Manipulation”, CoRL, 2022.
  14. R. Zhang, C. Yu, J. Chen, C. Fan and S. Gao, "Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding", NeurIPS, 2022.
  15. Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, “DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation”, Proc. CoRL, PMLR 205:594-605, 2023.
  16. Y. Qin, H. Su and X. Wang, “From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation”, RA-L, 2022.
  17. 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”, Proc. ECCV, 2022
  18. E. Sebastián, T. Duong, N. Atanasov, E. Montijano and C. Sagues, “LEMURS: Learning Distributed Multi-Robot Interactions”, IEEE Intl. Conf. on Robotics and Automation (ICRA), 2023.
  19. 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, in submission.
  20. I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, “Active Collaborative Localization in Heterogeneous Robot Teams,” Proc. Robotics Science and Systems, to appear 2023.
  21. Y. Tao, Y. Wu, F. Cladera and V. Kumar, “SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain,” Intl. Conf. on Robotics and Automation, 2023, to appear.
  22. Y. Wu, J. Wang and X. Wang, “Learning Generalizable Dexterous Manipulation from Human Grasp Affordance”, CoRL, 2022.
  23. C. Yu, H. Yu and S. Gao, "Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation", CoRL (Conference on Robot Learning), 2022.
  24. Y. Xu, N. Hansen, Z. Wang, Y.-C. Chan, H. Su and Z. Tu, “On the Feasibility of CrossTask Transfer with Model-Based Reinforcement Learning”, Proc. ICLR, 2023.
  25. R. Yang, G. Yang and X. Wang, “Neural Volumetric Memory for Visual Locomotion Control”, Proc. CVPR, 2023
  26. J. Ye, J. Wang, B. Huang, Y. Qin and X. Wang, “Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations”, RA-L, 2023.
  27. Z-H. Yin, B. Huang, Y. Qin, Q. Chen and X. Wang, “Rotating without Seeing: Towards In-hand Dexterity through Touch”, Proc. RSS, 2023.
  28. S.W. Chen, T. Wang, N. Atanasov, V. Kumar and M. Morari, "Large scale model predictive control with neural networks and primal active sets," Automatica135 (2022), p. 109947.
  29. T. Duong and N. Atanasov, “Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features,” IEEE Control Systems Letters (L-CSS), 2022. (Link)
  30. N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation," Conf. on Neural Information Processing Systems, 2021.
  31. M. Hao, Y. Li, Z. Di, N. B. Gundavarapu and X. Wang, "Test-Time Personalization with a Transformer for Human Pose Estimation," Conf. on Neural Information Processing Systems, 2021.
  32. R. Jangir, N. Hansen, S. Ghosal, M. Jain and X. Wang, "Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation," Robotics and Automation Letters (RA-L), 2022. (Link)
  33. L. Jarin-Lipschitz, X. Liu, Y. Tao and V. Kumar, "Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests," IEEE Intl. Conf. on Robotics and Automation, 2022.
  34. 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 (OJCSYS), 2022.
  35. Z. Li, T. Duong and N. Atanasov, “Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics,” Learning for Dynamics and Control (L4DC), 2022. (Link)
  36. 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 Micro42(1) (2022), pp. 61–68.
  37. X. Liu, G. V. Nardari, F. C. Ojeda, Y. Tao, A. Zhou, T. Donnelly, C. Qu, S. W. Chen, R. A. F. Romero, C. J. Taylor and V. Kumar, "Large-scale autonomous flight with real-time semantic slam under dense forest canopy," IEEE Robotics and Automation Letters7(2) (2022), pp. 5512–5519.
  38. 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 Letters7(2) (2022), pp. 5615–5622.
  39. D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations," IEEE Robotics and Automation Letters7(2) (2022), pp. 5552–5559.
  40. H. Sanghvi and C. J. Taylor, “Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models,” IEEE Intl. Conf. on Robotics and Automation, 2022.
  41. K. Sun, S. Chaves, P. Martin and V. Kumar, "RTGNN: A novel approach to model stochastic traffic dynamics," IEEE Intl. Conf. on Robotics and Automation, 2022.
  42. J. Wang, H. Xu, M. Narasimhan and X. Wang, "Multi-Person 3D Motion Prediction with Multi-Range Transformers," Conf. on Neural Information Processing Systems, 2021.
  43. R. Yang, M. Zhang, N. Hansen, H. Xu and X. Wang, "Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers," Intl. Conf. on Learning Representations, 2022. (Link)
  44. L. Zhou and V. Kumar, “Robust multi-robot active target tracking against sensing and communication attacks,” American Control Conf. (ACC), 2022.
  45. M. Shan, Q. Feng, Y. Jau and N. Atanasov, "ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description," IEEE/CVF Intl. Conf. on Computer Vision (ICCV), 2021. (Link)
  46. T. Duong and N. Atanasov, "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control," Robotics: Science and Systems (RSS), 2021. (Link)