Publications acknowledging TILOS support (NSF CCF-2112665)

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," 2023 ASEE Annual Conference & Exposition, 2023. [Link]
    2. R. P. Uhlig, S. Jawad, B. Sinha, P. P. Dey and M. N. Amin, "Student Use of Artificial Intelligence to Write Technical Engineering Papers—Cheating or a Tool to Augment Learning," 2023 ASEE Annual Conference & Exposition, 2023. [Link]

Foundations

    1. Z. Luo, T. S. Hy, P. Tabaghi, M. Defferrard, E. Rezaei, R. M. Carey, R. Davis, R. Jain and Y. Wang, "DE-HNN: An effective neural model for Circuit Netlist representation," International Conference on Artificial Intelligence and Statistics, 2024. (Collaboration with Rajeev Jain's team at Qualcomm.) [Link]
    2. Y. Lin, Y. Ma, Y.-X. Wang, R. E. Redberg and Z. Bu, "Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy," The Twelfth International Conference on Learning Representations, 2024. [Link]
    3. X. Huang, H. Dong, H. Yifan, Y. Ma and T. Zhang, "Reverse Diffusion Monte Carlo," The Twelfth International Conference on Learning Representations, 2024. [Link]
    4. T. Le, L. Ruiz and S. Jegelka, "A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs," The Twelfth International Conference on Learning Representations, 2024. [Link]
    5. T. Brugère, Z. Wan and Y. Wang, "Distances for Markov chains, and their differentiation," International Conference on Algorithmic Learning Theory, 2024. [Link]
    6. S. Gupta, J. Robinson, D. Lim, S. Villar and S. Jegelka, "Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning," 12th International Conference on Learning Representations, 2024. [Link]
    7. S. Chen, P. Tabaghi and Y. Wang, "Learning ultrametric trees for optimal transport regression," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 18, pp. 20657-20665, 2024. [Link]
    8. R. Niu, D. Wu, K. Kim, Y.-A. Ma, D. Watson-Parris and R. Yu, "Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling," preprint (arXiv:2402.18846), 2024. (To appear at ICML 2024.)
    9. P. Tabaghi and Y. Wang, "Universal Representation of Permutation-Invariant Functions on Vectors and Tensors," International Conference on Algorithmic Learning Theory, 2024. [Link]
    10. O. Mangoubi and N. K. Vishnoi, "Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers," The Twelfth International Conference on Learning Representations, 2024. [Link]
    11. M. Black, Z. Wan, G. Mishne, A. Nayyeri and Y. Wang, "Comparing Graph Transformers via Positional Encodings," preprint (arXiv:2402.14202), 2024.
    12. K. Long, K. Tran, M. Leok and N. Atanasov, "Safe Stabilizing Control for Polygonal Robots in Dynamic Elliptical Environments," preprint (arXiv:2310.00273), 2024. (To appear at the 2024 American Control Conference.)
    13. K. Kim, Y. Ma and J. Gardner, "Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?" International Conference on Artificial Intelligence and Statistics, 2024. [Link]
    14. H. Vardhan, A. Ghosh and A. Mazumdar, "An Improved Federated Clustering Algorithm with Model-based Clustering," Transactions on Machine Learning Research, 2024. [Link]
    15. D. Wu, T. Ide, G. Kollias, J. Navratil, A. Lozano, N. Abe, Y. Ma and R. Yu, "Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes," International Conference on Artificial Intelligence and Statistics, 2024. [Link]
    16. 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.
    17. C. Zhou, R. Yu and Y. Wang, "On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers," International Conference on Artificial Intelligence and Statistics, 2024. [Link]
    18. B. Kiani, T. Le, H. Lawrence, S. Jegelka and M. Weber, "On the hardness of learning under symmetries," The Twelfth International Conference on Learning Representations, 2024. [Link]
    19. B. K. Tran, B. S. Southworth and M. Leok, "On Properties of Adjoint Systems for Evolutionary PDEs," preprint (arXiv:2404.02320), 2024.
    20. A. Radhakrishnan, D. Beaglehole, P. Pandit and M. Belkin, "Mechanism for feature learning in neural networks and backpropagation-free machine learning models," Science, 2024. [Link]
    21. A. Radhakrishnan, M. Belkin and D. Drusvyatskiy, "Linear Recursive Feature Machines provably recover low-rank matrices," preprint (arXiv:2401.04553), 2024.
    22. 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]
    23. X. Xia, G. Mishne and Y. Wang, "Implicit graphon neural representation," International Conference on Artificial Intelligence and Statistics, 2023. (Oral presentation; top 1.9% of submissions.) [Link]
    24. 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]
    25. 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]
    26. V. Duruisseaux and M. Leok, "Practical perspectives on symplectic accelerated optimization," Optimization Methods and Software, 2023. [Link]
    27. 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]
    28. S. Chen and Y. Wang, "Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions," Conference on Neural Information Processing Systems, 2023. [Link]
    29. 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]
    30. O. Mangoubi and N. K. Vishnoi, "Sampling from Structured Log-Concave Distributions via a Soft-Threshold Dikin Walk," Advances in Neural Information Processing Systems, 2023. [Link]
    31. O. Mangoubi and N. K. Vishnoi, "Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations," 36th Annual Conference on Learning Theory, 2023. [Link]
    32. N. Ghosh and M. Belkin, "A universal trade-off between the model size, test loss, and training loss of linear predictors," SIAM Journal on Mathematics of Data Science, vol. 5, no. 4, pp. 977-1004, 2023. [Link]
    33. N. Boehmer, L. E. Celis, L. Huang, A. Mehrotra and N. K. Vishnoi, "Subset selection based on multiple rankings in the presence of bias: Effectiveness of fairness constraints for multiwinner voting score functions," International Conference on Machine Learning, 2023. [Link]
    34. 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.
    35. 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]
    36. 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.
    37. L. Hui, M. Belkin and S. Wright, "Cut your losses with squentropy," International Conference on Machine Learning, 2023. [Link]
    38. L. E. Celis, A. Kumar, A. Mehrotra and N. K. Vishnoi, "Bias in Evaluation Processes: An Optimization-Based Model," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
    39. 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]
    40. 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]
    41. 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.
    42. 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]
    43. K. Ahn, X. Cheng, M. Song, C. Yun, A. Jadbabaie and S. Sra, "Linear attention is (maybe) all you need (to understand transformer optimization)," NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 2023. [Link]
    44. K. Ahn, X. Cheng, H. Daneshmand and S. Sra, "Transformers learn to implement preconditioned gradient descent for in-context learning," Thirty-seventh Conference on Neural Information Processing Systems, 2023. [Link]
    45. 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.
    46. 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]
    47. G. Mishne, Z. Wan, Y. Wang and S. Yang, "The numerical stability of hyperbolic representation learning," International Conference on Machine Learning, 2023. [Link]
    48. E. Lei, Y. B. Uslu, H. Hassani and S. S. Bidokhti, "Text + Sketch: Image Compression at Ultra Low Rates," preprint (arXiv:2307.01944), 2023.
    49. E. Lei, H. Hassani and S. S. Bidokhti, "On a Relation Between the Rate-Distortion Function and Optimal Transport," preprint (arXiv:2307.00246), 2023. (Tiny Papers at ICML 2023.)
    50. E. Lei, H. Hassani and S. S. Bidokhti, "Federated neural compression under heterogeneous data," 2023 IEEE International Symposium on Information Theory (ISIT), 2023. (Collaboration with Hamed Hassani, Foundations team.) [Link]
    51. D. X. Wu, C. Yun and S. Sra, "On the Training Instability of Shuffling SGD with Batch Normalization," Proceedings of the 40th International Conference on Machine Learning, 2023. (Work with David Wu, co-supervised by the other authors.) [Link]
    52. 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]
    53. D. Wu, R. Niu, M. Chinazzi, Y. Ma and R. Yu, "Disentangled Multi-Fidelity Deep Bayesian Active Learning," 40th International Conference on Machine Learning, 2023. [Link]
    54. 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]
    55. D. Lee, B. Moniri, X. Huang, E. Dobriban and H. Hassani, "Demystifying disagreement-on-the-line in high dimensions," International Conference on Machine Learning, 2023. [Link]
    56. 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]
    57. C.-Y. Chuang, S. Jegelka and D. Alvarez-Melis, "InfoOT: Information maximizing optimal transport," International Conference on Machine Learning, 2023. [Link]
    58. C. Liu, A. Abedsoltan and M. Belkin, "On Emergence of Clean-Priority Learning in Early Stopped Neural Networks," preprint (arXiv:2306.02533), 2023.
    59. C. Cai, T. S. Hy, R. Yu and Y. Wang, "On the connection between mpnn and graph transformer," International Conference on Machine Learning, 2023.
    60. B. Tahmasebi, D. Lim and S. Jegelka, "The Power of Recursion in Graph Neural Networks for Counting Substructures," International Conference on Artificial Intelligence and Statistics, 2023. (Oral presentation; top 1.9% of submissions.) [Link]
    61. 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]
    62. 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]
    63. 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.
    64. 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]
    65. A. Roy, G. So and Y.-A. Ma, "Optimization on Pareto sets: On a theory of multi-objective optimization," preprint (arXiv:2308.02145), 2023.
    66. A. Radhakrishnan, M. Belkin and C. Uhler, "Wide and deep neural networks achieve consistency for classification," Proceedings of the National Academy of Sciences, vol. 120, no. 14, 2023. [Link]
    67. A. Mitra, G. J. Pappas and H. Hassani, "Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning," preprint (arXiv:2301.00944), 2023.
    68. 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]
    69. 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.
    70. A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," 39th International Symposium on Computational Geometry (SoCG 2023), 2023. [Link]
    71. A. Abedsoltan, M. Belkin, P. Pandit and L. Rademacher, "On the Nystrom Approximation for Preconditioning in Kernel Machines," preprint (arXiv:2312.03311), 2023.
    72. 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]
    73. 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]
    74. Y. Zhai and S. Gao, "Monte Carlo Tree Descent for Black-Box Optimization," Advances in Neural Information Processing Systems, 2022. [Link]
    75. 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]
    76. 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]
    77. X. Cheng, J. Zhang and S. Sra, "Theory and Algorithms for Diffusion Processes on Riemannian Manifolds," preprint (arXiv:2204.13665), 2022.
    78. X. Cheng, J. Zhang and S. Sra, "Efficient Sampling on Riemannian Manifolds via Langevin MCMC," Advances in Neural Information Processing Systems, 2022. [Link]
    79. W. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular maximization in clean linear time," Advances in Neural Information Processing Systems, 2022. [Link]
    80. V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent Min-Max Optimization Algorithm," International Conference on Machine Learning, 2022. [Link]
    81. S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal rates for interactive learning," Advances in Neural Information Processing Systems, 2022. [Link]
    82. 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]
    83. S. Chen, S. Lim, F. Mémoli, Z. Wan and Y. Wang, "Weisfeiler-Lehman meets Gromov-Wasserstein," International Conference on Machine Learning, 2022. [Link]
    84. R. Shen, L. Gao and Y.-A. Ma, "On Optimal Early Stopping: Over-informative versus Under-informative Parametrization," preprint (arXiv:2202.09885), 2022.
    85. 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]
    86. O. Mangoubi and N. Vishnoi, "Sampling from log-concave distributions with infinity-distance guarantees," Advances in Neural Information Processing Systems, 2022. [Link]
    87. 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]
    88. 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]
    89. 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]
    90. 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]
    91. M. Weber and S. Sra, "On a class of geodesically convex optimization problems solved via Euclidean MM methods," preprint (arXiv:2206.11426), 2022.
    92. M. Weber and S. Sra, "Computing Brascamp-Lieb Constants through the lens of Thompson Geometry," preprint (arXiv:2208.05013), 2022.
    93. 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.
    94. J. Jin and S. Sra, "Understanding Riemannian acceleration via a proximal extragradient framework," Conference on Learning Theory, 2022. [Link]
    95. 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]
    96. I. Han, M. Gartrell, E. Dohmatob and A. Karbasi, "Scalable MCMC sampling for nonsymmetric determinantal point processes," International Conference on Machine Learning, 2022. [Link]
    97. 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]
    98. 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.
    99. 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]
    100. E. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems," Advances in Neural Information Processing Systems, 2022. [Link]
    101. 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]
    102. 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%)
    103. 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]
    104. C. Yun, S. Rajput and S. Sra, "Minibatch vs Local {SGD} with Shuffling: Tight Convergence Bounds and Beyond," International Conference on Learning Representations, 2022. [Link]
    105. 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]
    106. C. Cai and Y. Wang, "Convergence of invariant graph networks," International Conference on Machine Learning, 2022. [Link]
    107. A. Radhakrishnan, G. Stefanakis, M. Belkin and C. Uhler, "Simple, fast, and flexible framework for matrix completion with infinite width neural networks," Proceedings of the National Academy of Sciences, vol. 119, no. 16, 2022. [Link]
    108. 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]
    109. A. Mehrotra and N. Vishnoi, "Fair ranking with noisy protected attributes," Advances in Neural Information Processing Systems, 2022. [Link]
    110. 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]
    111. A. Ghosh, A. Mazumdar, et al., "An Improved Algorithm for Clustered Federated Learning," preprint (arXiv:2210.11538), 2022.
    112. S. Jegelka, "Theory of graph neural networks: Representation and learning," The International Congress of Mathematicians, 2022. [Link]
    113. 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.
    114. 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]
    115. 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]
    116. K. Gatmiry, S. Jegelka and J. Kelner, "Optimization and Adaptive Generalization of Three-layer Neural Networks," International Conference on Learning Representations, 2021. [Link]
    117. 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]
    118. 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]
    119. 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.)
    120. 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]
    121. S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," preprint (arXiv:2112.00056), 2021.
    122. M. Belkin, "Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, 2021. [Link]

Networks

    1. X. Chen, N. NaderiAlizadeh, A. Ribeiro and S. S. Bidokhti, "Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks," preprint (arXiv:2404.03227), 2024.
    2. A. Ghosh, A. Sankararaman, K. Ramchandran, T. Javidi and A. Mazumdar, "Competing Bandits in Non-Stationary Matching Markets," IEEE Transactions on Information Theory, 2024. [Link]
    3. 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]
    4. 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]
    5. 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.
    6. 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]
    7. 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]
    8. 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]
    9. M. Lee, O. S. Haddadin and T. Javidi, "FFT-Based Approximations for Black-Box Optimization," 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023. [Link]
    10. J. Elenter, N. NaderiAlizadeh, T. Javidi and A. Ribeiro, "Primal-Dual Continual Learning: Stability and Plasticity through Lagrange Multipliers," preprint (arXiv:2310.00154), 2023.
    11. J. Cerviño, L. Ruiz and A. Ribeiro, "Learning by Transference: Training Graph Neural Networks on Growing Graphs," IEEE Transactions on Signal Processing, 2023. [Link]
    12. 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]
    13. 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]
    14. 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]
    15. I. Hounie, A. Ribeiro and L. F. O. Chamon, "Resilient Constrained Learning," preprint (arXiv:2306.02426), 2023.
    16. 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]
    17. 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]
    18. S. Shekhar and T. Javidi, "Instance Dependent Regret Analysis of Kernelized Bandits," International Conference on Machine Learning, 2022. [Link]
    19. R. Arghal, E. Lei and S. S. Bidokhti, "Robust graph neural networks via probabilistic lipschitz constraints," Learning for Dynamics and Control Conference, 2022. [Link]
    20. 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]
    21. 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]
    22. 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]
    23. X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-peer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172-182, 2022. [Link]

Chip Design

    1. Z. Xiong, R. S. Rajarathnam and D. Z. Pan, "A Data-Driven, Congestion-Aware and Open-Source Timing-Driven FPGA Placer Accelerated by GPUs," The 32nd IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2024. (Collaboration with AMD. Best paper award.) [Link]
    2. Y. Pan, M. Zhou, C. Lee, Z. Li, R. Kushwah, V. Narayanan and T. Rosing, "PRIMATE: Processing in Memory Acceleration for Dynamic Token-pruning Transformers," 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 2024. [Link]
    3. X. Yu, A. Thomas, I. G. Moreno, L. Gutierrez and T. Rosing, "Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing," ACM/IEEE International Conference on Information Processing in Sensor Networks, 2024. [Link]
    4. 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]
    5. W. Xu, P.-K. Hsu, N. Moshiri, S. Yu and T. Rosing, "HyperGen: Compact and Efficient Genome Sketching using Hyperdimensional Vectors," preprint, 2024. (Submitted to Bioinformatics.) [Link]
    6. S. Choi, J. Jung, A. B. Kahng, M. Kim, C.-H. Park, B. Pramanik and D. Yoon, "PROBE3.0: A Systematic Framework for Design-Technology Pathfinding With Improved Design Enablement," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 43, no. 4, pp. 1218-1231, 2024. [Link]
    7. R. Zhang, R. S. Rajarathnam, D. Z. Pan and F. Koushanfar, "ICMarks: A Robust Watermarking Framework for Integrated Circuit Physical Design IP Protection," preprint (arXiv:2404.18407), 2024.
    8. I. G. Moreno, X. Yu and T. Rosing, "KalmanHD: Robust On-Device Time Series Forecasting with Hyperdimensional Computing," 29th Asia and South Pacific Design Automation Conference, 2024. [Link]
    9. H. Chae, K. Zhu, B. Mutnury, Z. Jiang, D. De Araujo, D. Wallace, D. Winterberg, A. Klivans and D. Z. Pan, "ISOP-Yield: Yield-Aware Stack-Up Optimization for Advanced Package using Machine Learning," 29th Asia and South Pacific Design Automation Conference, 2024. [Link]
    10. H. Chae, K. Zhu, B. Mutnury, D. Wallace, D. Winterberg, D. De Araujo, J. Reddy, A. Klivans and D. Z. Pan, "ISOP+: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024. [Link]
    11. A. B. Kahng, R. R. Nerem, Y. Wang and C.-Y. Yang, "NN-Steiner: A mixed neural-algorithmic approach for the rectilinear Steiner minimum tree problem," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 12, pp. 13022-13030, 2024. [Link]
    12. A. B. Kahng, A. Mazumdar, J. Reeves and Y. Wang, "The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics," AI Magazine, 2024. [Link]
    13. Y. Nam, M. Zhou, S. Gupta, G. De Micheli, R. Cammarota, C. Wilkerson, D. Micciancio and T. Rosing, "Efficient Machine Learning on Encrypted Data Using Hyperdimensional Computing," IEEE/ACM International Symposium on Low Power Electronics and Design, 2023. [Link]
    14. W. Xu, V. Swaminathan, S. Pinge, S. Fuhrman and T. Rosing, "HyperMetric: Robust Hyperdimensional Computing on Error-prone Memories using Metric Learning," IEEE 41st International Conference on Computer Design, 2023. [Link]
    15. V. A. Chhabria, W. Jiang, A. B. Kahng and S. S. Sapatnekar, "A Machine Learning Approach to Improving Timing Consistency between Global Route and Detailed Route," Association for Computing Machinery, 2023. [Link]
    16. T. Zhang, A. González, N. Moshiri, R. Knight and T. Rosing, "GenoMiX: Accelerated Simultaneous Analysis of Human Genomics, Microbiome Metagenomics, and Viral Sequences," 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2023. [Link]
    17. T. Wang, S. Herbert and S. Gao, "Fractal Landscapes in Policy Optimization," Advances in Neural Information Processing Systems, vol. 36, 2023. [Link]
    18. S. Hussain, T. Huster, C. Mesterharm, P. Neekhara and F. Koushanfar, "ReFace: Adversarial Transformation Networks for Real-time Attacks on Face Recognition Systems," 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2023. (Collaboration with Todd Huster of Peraton Labs.) [Link]
    19. 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]
    20. R. Zhang, M. Javaheripi, Z. Ghodsi, A. Bleiweiss and F. Koushanfar, "AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs," 60th ACM/IEEE Design Automation Conference, 2023. (Collaboration with Amit Bleiweiss of Intel.) [Link]
    21. Q. Zhao, A. Thomas, A. Brin, X. Yu and T. Rosing, "Unleashing Hyperdimensional Computing with Nyström Method based Encoding," preprint, 2023. [Link]
    22. M. Zhou, Y. Nam, P. Gangwar, W. Xu, A. Dutta, K. Subramanyam, C. Wilkerson, R. Cammarota, S. Gupta and T. Rosing, "FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption," preprint (arXiv:2311.16293), 2023. (Collaboration with at Chris Wilkerson and Rosario Cammarota of Intel and Saransh Gupta of IBM.)
    23. M. Timken, O. Gungor, T. Rosing and B. Aksanli, "Analysis of Machine Learning Algorithms for Cyber Attack Detection in SCADA Power Systems," International Conference on Smart Applications, Communications and Networking, 2023. [Link]
    24. K. Babel, M. Javaheripi, Y. Ji, M. Kelkar, F. Koushanfar and A. Juels, "Lanturn: Measuring Economic Security of Smart Contracts Through Adaptive Learning," Association for Computing Machinery, 2023. [Link]
    25. 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]
    26. J. Jung, A. B. Kahng, S. Kundu, Z. Wang and D. Yoon, "Invited Paper: IEEE CEDA DATC Emerging Foundations in IC Physical Design and MLCAD Research," 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023. (Collaboration with Jinwook Jung of IBM.) [Link]
    27. J. Hu and A. B. Kahng, "The Inevitability of AI Infusion Into Design Closure and Signoff," 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023. [Link]
    28. I. Bustany, G. Gasparyan, A. B. Kahng, I. Koutis, B. Pramanik and Z. Wang, "An Open-Source Constraints-Driven General Partitioning Multi-Tool for VLSI Physical Design," 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023. (Collaboration with Ismail Bustany and Grigor Gasparyan of AMD.) [Link]
    29. H. Yu, C. Hirayama, C. Yu, S. Herbert and S. Gao, "Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance," IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023. [Link]
    30. 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]
    31. H. Chen, C. Fu, B. D. Rouhani, J. Zhao and F. Koushanfar, "Intellectual Property Protection of Deep Learning Systems via Hardware/Software Co-design," IEEE Design & Test, 2023. (Collaboration with Bita Darvish Rouhani of Microsoft.) [Link]
    32. D. Liu, K. Ergun and T. Š. Rosing, "Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification," IEEE International Conference on Acoustics, Speech and Signal Processing, 2023. [Link]
    33. 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]
    34. C.-K. Cheng, A. B. Kahng, B. Lin, Y. Wang and D. Yoon, "Gear-Ratio-Aware Standard Cell Layout Framework for DTCO Exploration," Association for Computing Machinery, 2023. [Link]
    35. 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]
    36. A. B. Kahng, S. Thumathy and M. Woo, "An Effective Cost-Skew Tradeoff Heuristic for VLSI Global Routing," 24th International Symposium on Quality Electronic Design, 2023. [Link]
    37. 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]
    38. 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]
    39. R. S. Rajarathnam, M. B. Alawieh, Z. Jiang, M. Iyer and D. Z. Pan, "DREAMPlaceFPGA: An open-source analytical placer for large scale heterogeneous FPGAs using deep-learning toolkit," 27th Asia and South Pacific Design Automation Conference, 2022. (Collaboration with Mahesh Iyer of Intel.) [Link]
    40. 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]
    41. K. Zhu, H. Chen, M. Liu, X. Tang, W. Shi, N. Sun and D. Z. Pan, "Generative-adversarial-network-guided well-aware placement for analog circuits," 27th Asia and South Pacific Design Automation Conference, 2022. [Link]
    42. K. Zhu, H. Chen, M. Liu and D. Z. Pan, "Automating analog constraint extraction: From heuristics to learning," 27th Asia and South Pacific Design Automation Conference, 2022. (Invited paper.) [Link]
    43. 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]
    44. H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. Sapatnekar, Z. Wang and Z. Zeng, "An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators," ACM/IEEE International Symposium on Machine Learning for CAD, 2022. [Link]
    45. H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. S. Sapatnekar, Z. Wang, et al., "Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms," Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022. [Link]
    46. A. F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini, A. Goldie and D. Z. Pan, "Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives," 27th Asia and South Pacific Design Automation Conference, 2022. (Invited paper.) [Link]
    47. 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]
    48. 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]
    49. A. B. Kahng, "Leveling up: A trajectory of OpenROAD, TILOS, and beyond," Proceedings of the 2022 International Symposium on Physical Design, 2022. [Link]
    50. 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]
    51. A. B. Kahng and Z. Wang, "ML for Design QoR Prediction," Machine Learning Applications in Electronic Design Automation, 2022. [Link]
    52. 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]
    53. J. Jung, A. B. Kahng, S. Kim and R. Varadarajan, "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD ICCAD Special Session Paper," IEEE/ACM International Conference On Computer Aided Design, 2021. (Invited paper.) [Link]
    54. J. Chen, I. H.-R. Jiang, J. Jung, A. B. Kahng, S. Kim, V. N. Kravets, Y.-L. Li, R. Varadarajan and M. Woo, "DATC RDF-2021: Design flow and beyond iccad special session paper," IEEE/ACM International Conference On Computer Aided Design, 2021. (Invited paper.) [Link]
    55. C.-K. Cheng, A. B. Kahng, I. Kang, M. Kim, D. Lee, B. Lin, D. Park and M. Woo, "Core-eco: Concurrent refinement of detailed place-and-route for an efficient eco automation," IEEE 39th International Conference on Computer Design, 2021. [Link]

Robotics

    1. Y. Wu, Y. Tao, I. Spasojevic and V. Kumar, "Trajectory Optimization with Global Yaw Parameterization for Field-of-View Constrained Autonomous Flight," preprint (arXiv:2403.17067), 2024. (Submitted to IEEE International Conference on Intelligent Robots and Systems 2024.)
    2. Y. Tao, X. Liu, I. Spasojevic, S. Agarwal and V. Kumar, "3D Active Metric-Semantic SLAM," IEEE Robotics and Automation Letters, 2024. [Link]
    3. Y. S. Shao, Y. Wu, L. Jarin-Lipschitz, P. Chaudhari and V. Kumar, "Design and Evaluation of Motion Planners for Quadrotors in Environments with Varying Complexities," preprint (arXiv:2309.13720), 2024. (To appear at IEEE ICRA 2024.)
    4. T. Duong, A. Altawaitan, J. Stanley and N. Atanasov, "Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control," preprint (arXiv:2401.09520), 2024. (Submitted to IEEE Transactions on Robotics.)
    5. N. Hansen, H. Su and X. Wang, "TD-MPC2: Scalable, Robust World Models for Continuous Control," International Conference on Learning Representations, 2024. (ICLR 2024 spotlight.) [Link]
    6. M. V. T, P. Wang, Z. Fan, Z. Wang, H. Su and R. Ramamoorthi, "Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D," Computer Vision and Pattern Recognition Conference, 2024. [Link]
    7. K. Mao, I. Spasojevic, M. A. Hsieh and V. Kumar, "TOPPQuad: Dynamically-Feasible Time Optimal Path Parametrization for Quadrotors," preprint (arXiv:2309.11637), 2024.
    8. K. Long, J. Cortes and N. Atanasov, "Distributionally Robust Policy and Lyapunov-Certificate Learning," preprint (arXiv:2404.03017), 2024. (Submitted to IEEE Open Journal of Control Systems.)
    9. H. Zhang, A. Srikanthan, S. Folk, V. Kumar and N. Matni, "Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems," preprint (arXiv:2401.04960), 2024. (Submitted to Learning for Dynamics & Control (L4DC) 2024.)
    10. F. Cladera, I. D. Miller, Z. Ravichandran, V. Murali, J. Hughes, M. A. Hsieh, C. J. Taylor and V. Kumar, "Challenges and Opportunities for Large-Scale Exploration with Air-Ground Teams using Semantics," preprint (arXiv:2405.07169), 2024.
    11. A. Prabhu, X. Liu, I. Spasojevic, Y. Wu, Y. Shao, D. Ong, J. Lei, P. C. Green, P. Chaudhari and V. Kumar, "UAVs for forestry: Metric-semantic mapping and diameter estimation with autonomous aerial robots," Mechanical Systems and Signal Processing, 2024. [Link]
    12. Y.-H. Wu, J. Wang and X. Wang, "Learning generalizable dexterous manipulation from human grasp affordance," Conference on Robot Learning, 2023. [Link]
    13. 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]
    14. Y. Tao, E. Iceland, B. Li, E. Zwecher, U. Heinemann, A. Cohen, A. Avni, O. Gal, A. Barel and V. Kumar, "Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles," preprint (arXiv:2309.06986), 2023. (Submitted to IEEE ICRA 2024.)
    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," Conference on Robot Learning, 2023. [Link]
    16. 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]
    17. 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.
    18. S. Kumar, J. Zamora, N. Hansen, R. Jangir and X. Wang, "Graph inverse reinforcement learning from diverse videos," Conference on Robot Learning, 2023. (Oral presentation.) [Link]
    19. 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]
    20. 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]
    21. 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]
    22. L. Zhou and V. Kumar, "Robust multi-robot active target tracking against sensing and communication attacks," IEEE Transactions on Robotics, 2023. [Link]
    23. 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]
    24. 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]
    25. 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]
    26. J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang and S. De Mello, "Open-Vocabulary Panoptic Segmentation With Text-to-Image Diffusion Models," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. (Collaboration with Sifei Liu, Arash Vahdat, Wonmin Byeon, and Shalini De Mello of NVIDIA.) [Link]
    27. 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]
    28. J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, H. Su, Y. Tang, S. Tao, X. Wei, Y. Yao, et al., "ManiSkill2: A unified benchmark for generalizable manipulation skills," preprint (arXiv:2302.04659), 2023.
    29. I. Spasojevic, X. Liu, A. Prabhu, A. Ribeiro, G. J. Pappas and V. Kumar, "Robust localization of aerial vehicles via active control of identical ground vehicles," International Conference on Intelligent Robots and Systems, 2023. [Link]
    30. I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," Robotics: Science and Systems, 2023. [Link]
    31. I. Boero, I. Spasojevic, M. Del Castillo, G. Pappas, V. Kumar and A. Ribeiro, "Navigation with shadow prices to optimize multi-commodity flow rates," 62nd IEEE Conference on Decision and Control (CDC), 2023. [Link]
    32. 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.
    33. E. Sebastian, T. Duong, N. Atanasov, E. Montijano and C. Sagués, "Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks," IEEE International Symposium on Multi-Robot & Multi-Agent Systems, 2023. [Link]
    34. 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]
    35. E. Sebastian, T. Duong, N. Atanasov, E. Montijano and C. Sagues, "Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems," preprint (arXiv:2401.00212), 2023. (Submitted to IEEE Transactions on Robotics.)
    36. D. Cheng, F. C. Ojeda, A. Prabhu, X. Liu, A. Zhu, P. C. Green, R. Ehsani, P. Chaudhari and V. Kumar, "TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards," preprint (arXiv:2310.02162), 2023. (Submitted to IEEE ICRA 2024.)
    37. 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]
    38. 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]
    39. 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.
    40. X. Wang, N. Heydaribeni, F. Koushanfar and T. Javidi, "Federated Certainty Equivalence Control for Linear Gaussian Systems with Unknown Decoupled Dynamics and Quadratic Common Cost," 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2023. [Link]
    41. 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]
    42. 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.
    43. 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]
    44. 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]
    45. 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]
    46. 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]
    47. X. Liu, G. V. Nardari, F. C. Ojeda, Y. Tao, A. Zhou, T. Donnelly, C. Qu, S. W. Chen, R. A. Romero, C. J. Taylor, et al., "Large-scale autonomous flight with real-time semantic slam under dense forest canopy," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5512-5519, 2022. [Link]
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