Education and Workforce Development

1. 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. 3. 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, pp. 719-724. [Link]
   4. X. Xia, G. Mishne and Y. Wang, "Implicit graphon neural representation," International Conference on Artificial Intelligence and Statistics, 2023, pp. 10619-10634. (Oral presentation; top 1.9% of submissions.) [Link]
   5. D. X. Wu, C. Yun and S. Sra, "On the training instability of shuffling SGD with batch normalization," International Conference on Machine Learning, 2023, pp. 37787-37845. (Work with David Wu, co-supervised by the other authors.) [Link]
   6. D. Wu, R. Niu, M. Chinazzi, Y. Ma and R. Yu, "Disentangled Multi-Fidelity Deep Bayesian Active Learning," preprint (arXiv:2305.04392), 2023.
   7. 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, pp. 51-63. [Link]
   8. 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, pp. 11023-11042. (Oral presentation; top 1.9% of submissions.) [Link]
   9. 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]
   10. A. Mitra, G. J. Pappas and H. Hassani, "Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning," preprint (arXiv:2301.00944), 2023.
   11. G. Mishne, Z. Wan, Y. Wang and S. Yang, "The numerical stability of hyperbolic representation learning," International Conference on Machine Learning, 2023, pp. 24925-24949. [Link]
   12. A. Mehrotra and N. K. Vishnoi, "Maximizing Submodular Functions for Recommendation in the Presence of Biases," Proceedings of the ACM Web Conference 2023, 2023, pp. 3625-3636. [Link]
   13. 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.
   14. 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]
   15. D. Lee, B. Moniri, X. Huang, E. Dobriban and H. Hassani, "Demystifying Disagreement-on-the-Line in High Dimensions," preprint (arXiv:2301.13371), 2023.
   16. 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, pp. 15828-15860. [Link]
   17. L. Hui, M. Belkin and S. Wright, "Cut your losses with squentropy," preprint (arXiv:2302.03952), 2023.
   18. A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," preprint (arXiv:2302.03771), 2023.
   19. V. Duruisseaux and M. Leok, "Practical perspectives on symplectic accelerated optimization," Optimization Methods and Software, 2023, pp. 1-39. [Link]
   20. 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, pp. 1-12. [Link]
   21. M. Black, Z. Wan, A. Nayyeri and Y. Wang, "Understanding oversquashing in gnns through the lens of effective resistance," International Conference on Machine Learning, 2023, pp. 2528-2547. [Link]
   22. 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.
   23. C. Cai, T. S. Hy, R. Yu and Y. Wang, "On the connection between MPNN and graph transformer," preprint (arXiv:2301.11956), 2023.
   24. C.-Y. Chuang, S. Jegelka and D. Alvarez-Melis, "InfoOT: Information maximizing optimal transport," International Conference on Machine Learning, 2023, pp. 6228-6242. [Link]
   25. A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," preprint (arXiv:2302.03771), 2023.
   26. 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, vol. 59, pp. 85-116, 2022. [Link]
   27. R. Shen, L. Gao and Y.-A. Ma, "On Optimal Early Stopping: Over-informative versus Under-informative Parametrization," preprint (arXiv:2202.09885), 2022.
   28. S. Jegelka, "Theory of graph neural networks: Representation and learning," preprint (arXiv:2204.07697), 2022.
   29. 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.
   30. P. H. Zadeh and S. Sra, "Introducing discrepancy values of matrices with application to bounding norms of commutators," Linear Algebra and its Applications, vol. 651, pp. 359-384, 2022. (Work with PhD student Pourya Zadeh supervised by Sra.) [Link]
   31. 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, pp. 2029-2038. [Link]
   32. M. Weber and S. Sra, "Computing Brascamp-Lieb Constants through the lens of Thompson Geometry," preprint (arXiv:2208.05013), 2022.
   33. 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.
   34. M. Weber and S. Sra, "On a class of geodesically convex optimization problems solved via Euclidean MM methods," preprint (arXiv:2206.11426), 2022.
   35. 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, pp. 817-841. [Link]
   36. 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]
   37. 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.
   38. 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]
   39. A. Mehrotra and N. Vishnoi, "Fair ranking with noisy protected attributes," Advances in Neural Information Processing Systems, vol. 35, pp. 31711-31725, 2022. [Link]
   40. 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, pp. 599-609. [Link]
   41. O. Mangoubi and N. Vishnoi, "Re-analyze Gauss: Bounds for private matrix approximation via Dyson Brownian motion," Advances in Neural Information Processing Systems, vol. 35, pp. 38585-38599, 2022. [Link]
   42. 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.
   43. O. Mangoubi and N. Vishnoi, "Sampling from log-concave distributions with infinity-distance guarantees," Advances in Neural Information Processing Systems, vol. 35, pp. 12633-12646, 2022. [Link]
   44. 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, vol. 35, pp. 1182-1195, 2022. [Link]
   45. 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%)
   46. W. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular maximization in clean linear time," Advances in Neural Information Processing Systems, vol. 35, pp. 17473-17487, 2022. [Link]
   47. S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal rates for interactive learning," Advances in Neural Information Processing Systems, vol. 35, pp. 28657-28669, 2022. [Link]
   48. 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, vol. 35, pp. 15338-15352, 2022. [Link]
   49. 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, vol. 35, pp. 20809-20822, 2022. [Link]
   50. J. Jin and S. Sra, "Understanding Riemannian acceleration via a proximal extragradient framework," Conference on Learning Theory, 2022, pp. 2924-2962. [Link]
   51. A. Ghosh, A. Mazumdar, et al., "An Improved Algorithm for Clustered Federated Learning," preprint (arXiv:2210.11538), 2022.
   52. 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.
   53. S. Hanneke, A. Karbasi, M. Mahmoody, I. Mehalel and S. Moran, "On optimal learning under targeted data poisoning," Advances in Neural Information Processing Systems, vol. 35, pp. 30770-30782, 2022. [Link]
   54. 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, vol. 35, pp. 35657-35671, 2022. [Link]
   55. I. Han, M. Gartrell, E. Dohmatob and A. Karbasi, "Scalable MCMC sampling for nonsymmetric determinantal point processes," International Conference on Machine Learning, 2022, pp. 8213-8229. [Link]
   56. 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]
   57. 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, pp. 386-395. [Link]
   58. D. Beaglehole, M. Belkin and P. Pandit, "Kernel Ridgeless Regression is Inconsistent for Low Dimensions," preprint (arXiv:2205.13525), 2022.
   59. C. Cai and Y. Wang, "Convergence of invariant graph networks," International Conference on Machine Learning, 2022, pp. 2457-2484. [Link]
   60. Y. Cao, Z. Chen, M. Belkin and Q. Gu, "Benign overfitting in two-layer convolutional neural networks," Advances in Neural Information Processing Systems, vol. 35, pp. 25237-25250, 2022. [Link]
   61. 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, vol. 35, pp. 34241-34257, 2022. [Link]
   62. S. Chen, S. Lim, F. Mémoli, Z. Wan and Y. Wang, "Weisfeiler-Lehman meets Gromov-Wasserstein," International Conference on Machine Learning, 2022, pp. 3371-3416. [Link]
   63. X. Cheng, J. Zhang and S. Sra, "Theory and Algorithms for Diffusion Processes on Riemannian Manifolds," preprint (arXiv:2204.13665), 2022.
   64. X. Cheng, J. Zhang and S. Sra, "Efficient Sampling on Riemannian Manifolds via Langevin MCMC," Advances in Neural Information Processing Systems, vol. 35, pp. 5995-6006, 2022. [Link]
   65. Y. Zhai and S. Gao, "Monte Carlo Tree Descent for Black-Box Optimization," Advances in Neural Information Processing Systems, vol. 35, pp. 12581-12593, 2022. [Link]
   66. 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, vol. 35, pp. 2944-2957, 2022. [Link]
   67. M. Belkin, "Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, vol. 30, pp. 203-248, 2021. [Link]
   68. 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]
   69. S. Pal, A. Mazumdar and V. Gandikota, "Support recovery of sparse signals from a mixture of linear measurements," Advances in Neural Information Processing Systems, vol. 34, pp. 19082-19094, 2021. [Link]
   70. 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]
   71. C. Yun, S. Rajput and S. Sra, "Minibatch vs local SGD with shuffling: Tight convergence bounds and beyond," preprint (arXiv:2110.10342), 2021.
   72. S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," preprint (arXiv:2112.00056), 2021.
   73. 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, vol. 34, pp. 4974-4986, 2021. [Link]
   74. 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, vol. 34, pp. 23023-23035, 2021. [Link]
   75. K. Gatmiry, S. Jegelka and J. Kelner, "Optimization and Adaptive Generalization of Three-layer Neural Networks," International Conference on Learning Representations, 2021. [Link]
   76. 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.)
   77. V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A convergent and dimension-independent min-max optimization algorithm," preprint (arXiv:2006.12376), 2020.

Networks

1. 78. M. Lee, O. S. Haddadin and T. Javidi, "FFT-Based Approximations for Black-Box Optimization," 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023, pp. 205-209. [Link]
   79. 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]
   80. V. Kungurtsev, A. Cobb, T. Javidi and B. Jalaian, "Decentralized bayesian learning with metropolis-adjusted hamiltonian monte carlo," Machine Learning, 2023, pp. 1-29. [Link]
   81. 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]
   82. 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, pp. 312-313. [Link]
   83. 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, pp. 236-248. [Link]
   84. 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]
   85. 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.)
   86. E. Lei, H. Hassani and S. S. Bidokhti, "On a Relation Between the Rate-Distortion Function and Optimal Transport," On a Relation Between the Rate-Distortion Function and Optimal Transport, 2023. (Collaboration with Hamed Hassani, Foundations team.) [Link]
   87. 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.
   88. J. Cerviño, L. Ruiz and A. Ribeiro, "Learning by Transference: Training Graph Neural Networks on Growing Graphs," IEEE Transactions on Signal Processing, vol. 71, pp. 233-247, 2023. [Link]
   89. 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, pp. 3815-3854. [Link]
   90. 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, pp. 791-793. [Link]
   91. 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]
   92. S. Shekhar and T. Javidi, "Instance Dependent Regret Analysis of Kernelized Bandits," International Conference on Machine Learning, 2022, pp. 19747-19772. [Link]
   93. 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, pp. 288-293. [Link]
   94. 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, pp. 97-104. [Link]
   95. R. Arghal, E. Lei and S. S. Bidokhti, "Robust graph neural networks via probabilistic lipschitz constraints," Learning for Dynamics and Control Conference, 2022, pp. 1073-1085. [Link]

Chip Design

1. 96. 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]
   97. 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, pp. 1-8. [Link]
   98. 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, pp. 158-166. (Invited paper.) [Link]
   99. 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, pp. 70-75. [Link]
   100. A. B. Kahng, "Leveling up: A trajectory of OpenROAD, TILOS, and beyond," Proceedings of the 2022 International Symposium on Physical Design, 2022, pp. 73-79. [Link]
   101. 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, pp. 519-525. [Link]
   102. 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, pp. 108-113. (Invited paper.) [Link]
   103. 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, pp. 300-306. [Link]
   104. 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, pp. 3-11. [Link]
   105. 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, pp. 500-505. (Invited paper.) [Link]
   106. 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, pp. 1-9. [Link]
   107. A. B. Kahng and Z. Wang, "ML for Design QoR Prediction," Machine Learning Applications in Electronic Design Automation, 2022, pp. 3-33. [Link]
   108. 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]
   109. 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, pp. 1-6. (Invited paper.) [Link]
   110. 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, pp. 1-8. (Invited paper.) [Link]
   111. 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, pp. 135-141. [Link]
   112. 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, pp. 119-126. [Link]
   113. 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, pp. 7-14. [Link]
   114. 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, pp. 1-9. (Best paper award.) [Link]
   115. 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]
   116. 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, pp. 1-9. (Invited paper.) [Link]
   117. 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, pp. 366-373. [Link]
   118. 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, pp. 1-6. (Invited paper.) [Link]

Robotics

1. 119. L. Zhou and V. Kumar, "Robust multi-robot active target tracking against sensing and communication attacks," IEEE Transactions on Robotics, 2023. [Link]
   120. 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.
   121. 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]
   122. 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, pp. 1430-1440. [Link]
   123. C. Yu, H. Yu and S. Gao, "Learning control admissibility models with graph neural networks for multi-agent navigation," Conference on Robot Learning, 2023, pp. 934-945. [Link]
   124. Y.-H. Wu, J. Wang and X. Wang, "Learning generalizable dexterous manipulation from human grasp affordance," Conference on Robot Learning, 2023, pp. 618-629. [Link]
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