Publications acknowledging TILOS support (NSF CCF2112665)
Education and Workforce Development

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

 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. 719724. [Link]
 X. Xia, G. Mishne and Y. Wang, "Implicit graphon neural representation," International Conference on Artificial Intelligence and Statistics, 2023, pp. 1061910634. (Oral presentation; top 1.9% of submissions.) [Link]
 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. 3778737845. (Work with David Wu, cosupervised by the other authors.) [Link]
 D. Wu, R. Niu, M. Chinazzi, Y. Ma and R. Yu, "Disentangled MultiFidelity Deep Bayesian Active Learning," preprint (arXiv:2305.04392), 2023.
 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. 5163. [Link]
 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. 1102311042. (Oral presentation; top 1.9% of submissions.) [Link]
 A. Radhakrishnan, M. Belkin and C. Uhler, "Wide and deep neural networks achieve consistency for classification," Proceedings of the National Academy of Sciences, vol. 120, no. 14, pp. e2208779120, 2023. [Link]
 A. Mitra, G. J. Pappas and H. Hassani, "Temporal Difference Learning with Compressed Updates: ErrorFeedback meets Reinforcement Learning," preprint (arXiv:2301.00944), 2023.
 G. Mishne, Z. Wan, Y. Wang and S. Yang, "The numerical stability of hyperbolic representation learning," International Conference on Machine Learning, 2023, pp. 2492524949. [Link]
 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. 36253636. [Link]
 W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M. E. Khan and M. Schmidt, "Simplifying Momentumbased Riemannian Submanifold Optimization," preprint (arXiv:2302.09738), 2023.
 B. Li, Y. Ma, J. N. Kutz and X. Yang, "The adaptive spectral Koopman method for dynamical systems," SIAM Journal on Applied Dynamical Systems, vol. 22, no. 3, pp. 15231551, 2023. [Link]
 D. Lee, B. Moniri, X. Huang, E. Dobriban and H. Hassani, "Demystifying DisagreementontheLine in High Dimensions," preprint (arXiv:2301.13371), 2023.
 A. Karbasi, N. L. Kuang, Y. Ma and S. Mitra, "Langevin Thompson sampling with logarithmic communication: bandits and reinforcement learning," International Conference on Machine Learning, 2023, pp. 1582815860. [Link]
 L. Hui, M. Belkin and S. Wright, "Cut your losses with squentropy," preprint (arXiv:2302.03952), 2023.
 A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," preprint (arXiv:2302.03771), 2023.
 V. Duruisseaux and M. Leok, "Practical perspectives on symplectic accelerated optimization," Optimization Methods and Software, 2023, pp. 139. [Link]
 K. Bhatia, Y.A. Ma, A. D. Dragan, P. L. Bartlett and M. I. Jordan, "Bayesian robustness: A nonasymptotic viewpoint," Journal of the American Statistical Association, 2023, pp. 112. [Link]
 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. 25282547. [Link]
 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.
 C. Cai, T. S. Hy, R. Yu and Y. Wang, "On the connection between MPNN and graph transformer," preprint (arXiv:2301.11956), 2023.
 C.Y. Chuang, S. Jegelka and D. AlvarezMelis, "InfoOT: Information maximizing optimal transport," International Conference on Machine Learning, 2023, pp. 62286242. [Link]
 A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," preprint (arXiv:2302.03771), 2023.
 C. Liu, L. Zhu and M. Belkin, "Loss landscapes and optimization in overparameterized nonlinear systems and neural networks," Applied and Computational Harmonic Analysis, vol. 59, pp. 85116, 2022. [Link]
 R. Shen, L. Gao and Y.A. Ma, "On Optimal Early Stopping: Overinformative versus Underinformative Parametrization," preprint (arXiv:2202.09885), 2022.
 S. Jegelka, "Theory of graph neural networks: Representation and learning," preprint (arXiv:2204.07697), 2022.
 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.
 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. 359384, 2022. (Work with PhD student Pourya Zadeh supervised by Sra.) [Link]
 D. Wu, M. Chinazzi, A. Vespignani, Y.A. Ma and R. Yu, "Multifidelity hierarchical neural processes," Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 20292038. [Link]
 M. Weber and S. Sra, "Computing BrascampLieb Constants through the lens of Thompson Geometry," preprint (arXiv:2208.05013), 2022.
 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.
 M. Weber and S. Sra, "On a class of geodesically convex optimization problems solved via Euclidean MM methods," preprint (arXiv:2206.11426), 2022.
 Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Selfconsistency of the Fokker Planck equation," Conference on Learning Theory, 2022, pp. 817841. [Link]
 A. Radhakrishnan, G. Stefanakis, M. Belkin and C. Uhler, "Simple, fast, and flexible framework for matrix completion with infinite width neural networks," Proceedings of the National Academy of Sciences, vol. 119, no. 16, pp. e2115064119, 2022. [Link]
 K. E. Nikolakakis, F. Haddadpour, A. Karbasi and D. S. Kalogerias, "Beyond lipschitz: Sharp generalization and excess risk bounds for fullbatch gd," preprint (arXiv:2204.12446), 2022.
 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. 858884, 2022. [Link]
 A. Mehrotra and N. Vishnoi, "Fair ranking with noisy protected attributes," Advances in Neural Information Processing Systems, vol. 35, pp. 3171131725, 2022. [Link]
 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. 599609. [Link]
 O. Mangoubi and N. Vishnoi, "Reanalyze Gauss: Bounds for private matrix approximation via Dyson Brownian motion," Advances in Neural Information Processing Systems, vol. 35, pp. 3858538599, 2022. [Link]
 O. Mangoubi and N. K. Vishnoi, "Faster Sampling from LogConcave Distributions over Polytopes via a SoftThreshold Dikin Walk," preprint (arXiv:2206.09384), 2022.
 O. Mangoubi and N. Vishnoi, "Sampling from logconcave distributions with infinitydistance guarantees," Advances in Neural Information Processing Systems, vol. 35, pp. 1263312646, 2022. [Link]
 N. Mallinar, J. Simon, A. Abedsoltan, P. Pandit, M. Belkin and P. Nakkiran, "Benign, tempered, or catastrophic: Toward a refined taxonomy of overfitting," Advances in Neural Information Processing Systems, vol. 35, pp. 11821195, 2022. [Link]
 D. Lim, J. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron and S. Jegelka, "Sign and basis invariant networks for spectral graph representation learning," preprint (arXiv:2202.13013), 2022. (Spotlight/notable top 25%)
 W. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular maximization in clean linear time," Advances in Neural Information Processing Systems, vol. 35, pp. 1747317487, 2022. [Link]
 S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal rates for interactive learning," Advances in Neural Information Processing Systems, vol. 35, pp. 2865728669, 2022. [Link]
 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. 1533815352, 2022. [Link]
 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. 2080920822, 2022. [Link]
 J. Jin and S. Sra, "Understanding Riemannian acceleration via a proximal extragradient framework," Conference on Learning Theory, 2022, pp. 29242962. [Link]
 A. Ghosh, A. Mazumdar, et al., "An Improved Algorithm for Clustered Federated Learning," preprint (arXiv:2210.11538), 2022.
 E. Y. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for LongTerm Fairness in Decision Systems," preprint (arXiv:2210.12546), 2022.
 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. 3077030782, 2022. [Link]
 I. Han, A. Zandieh, J. Lee, R. Novak, L. Xiao and A. Karbasi, "Fast neural kernel embeddings for general activations," Advances in neural information processing systems, vol. 35, pp. 3565735671, 2022. [Link]
 I. Han, M. Gartrell, E. Dohmatob and A. Karbasi, "Scalable MCMC sampling for nonsymmetric determinantal point processes," International Conference on Machine Learning, 2022, pp. 82138229. [Link]
 Y. Freund, Y.A. Ma and T. Zhang, "When is the convergence time of Langevin algorithms dimension independent? A composite optimization viewpoint," The Journal of Machine Learning Research, vol. 23, no. 1, pp. 96049635, 2022. [Link]
 J. Dadashkarimi, A. Karbasi and D. Scheinost, "Combining multiple atlases to estimate datadriven mappings between functional connectomes using optimal transport," International Conference on Medical Image Computing and ComputerAssisted Intervention, 2022, pp. 386395. [Link]
 D. Beaglehole, M. Belkin and P. Pandit, "Kernel Ridgeless Regression is Inconsistent for Low Dimensions," preprint (arXiv:2205.13525), 2022.
 C. Cai and Y. Wang, "Convergence of invariant graph networks," International Conference on Machine Learning, 2022, pp. 24572484. [Link]
 Y. Cao, Z. Chen, M. Belkin and Q. Gu, "Benign overfitting in twolayer convolutional neural networks," Advances in Neural Information Processing Systems, vol. 35, pp. 2523725250, 2022. [Link]
 N. Chandramoorthy, A. Loukas, K. Gatmiry and S. Jegelka, "On the generalization of learning algorithms that do not converge," Advances in Neural Information Processing Systems, vol. 35, pp. 3424134257, 2022. [Link]
 S. Chen, S. Lim, F. Mémoli, Z. Wan and Y. Wang, "WeisfeilerLehman meets GromovWasserstein," International Conference on Machine Learning, 2022, pp. 33713416. [Link]
 X. Cheng, J. Zhang and S. Sra, "Theory and Algorithms for Diffusion Processes on Riemannian Manifolds," preprint (arXiv:2204.13665), 2022.
 X. Cheng, J. Zhang and S. Sra, "Efficient Sampling on Riemannian Manifolds via Langevin MCMC," Advances in Neural Information Processing Systems, vol. 35, pp. 59956006, 2022. [Link]
 Y. Zhai and S. Gao, "Monte Carlo Tree Descent for BlackBox Optimization," Advances in Neural Information Processing Systems, vol. 35, pp. 1258112593, 2022. [Link]
 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. 29442957, 2022. [Link]
 M. Belkin, "Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, vol. 30, pp. 203248, 2021. [Link]
 A. Ghosh, R. K. Maity, S. Kadhe, A. Mazumdar and K. Ramchandran, "Communicationefficient and byzantinerobust distributed learning with error feedback," IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 3, pp. 942953, 2021. [Link]
 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. 1908219094, 2021. [Link]
 W. Mou, Y.A. Ma, M. J. Wainwright, P. L. Bartlett and M. I. Jordan, "Highorder Langevin diffusion yields an accelerated MCMC algorithm," The Journal of Machine Learning Research, vol. 22, no. 1, pp. 19191959, 2021. [Link]
 C. Yun, S. Rajput and S. Sra, "Minibatch vs local SGD with shuffling: Tight convergence bounds and beyond," preprint (arXiv:2110.10342), 2021.
 S. Sra, "Positive definite functions of noncommuting contractions, HuaBellman matrices, and a new distance metric," preprint (arXiv:2112.00056), 2021.
 J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka and S. Sra, "Can contrastive learning avoid shortcut solutions?" Advances in neural information processing systems, vol. 34, pp. 49744986, 2021. [Link]
 E. McCarty, Q. Zhao, A. Sidiropoulos and Y. Wang, "NNBaker: A neuralnetwork infused algorithmic framework for optimization problems on geometric intersection graphs," Advances in Neural Information Processing Systems, vol. 34, pp. 2302323035, 2021. [Link]
 K. Gatmiry, S. Jegelka and J. Kelner, "Optimization and Adaptive Generalization of Threelayer Neural Networks," International Conference on Learning Representations, 2021. [Link]
 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 cosupervised by the other named authors.)
 V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A convergent and dimensionindependent minmax optimization algorithm," preprint (arXiv:2006.12376), 2020.
Networks

 M. Lee, O. S. Haddadin and T. Javidi, "FFTBased Approximations for BlackBox Optimization," 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023, pp. 205209. [Link]
 X. Zheng, T. Javidi and B. Touri, "ZerothOrder NonConvex Optimization for Cooperative MultiAgent Systems with Diminishing Step Size and Smoothing Radius," IEEE Control Systems Letters, 2023. [Link]
 V. Kungurtsev, A. Cobb, T. Javidi and B. Jalaian, "Decentralized bayesian learning with metropolisadjusted hamiltonian monte carlo," Machine Learning, 2023, pp. 129. [Link]
 V. Kungurtsev, M. Morafah, T. Javidi and G. Scutari, "Decentralized asynchronous nonconvex stochastic optimization on directed graphs," IEEE Transactions on Control of Network Systems, 2023. [Link]
 Q. Zhao, X. Yu and T. Rosing, "Attentive Multimodal Learning on Sensor Data using Hyperdimensional Computing," Proceedings of the 22nd International Conference on Information Processing in Sensor Networks, 2023, pp. 312313. [Link]
 X. Yu, L. Cherkasova, H. Vardhan, Q. Zhao, E. Ekaireb, X. Zhang, A. Mazumdar and T. Rosing, "AsyncHFL: 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. 236248. [Link]
 E. Lei, H. Hassani and S. S. Bidokhti, "Neural estimation of the ratedistortion function with applications to operational source coding," IEEE Journal on Selected Areas in Information Theory, 2023. [Link]
 E. Lei, H. Hassani and S. S. Bidokhti, "Federated Neural Compression Under Heterogeneous Data," preprint (arXiv:2305.16416), 2023. (Collaboration with Hamed Hassani, Foundations team.)
 E. Lei, H. Hassani and S. S. Bidokhti, "On a Relation Between the RateDistortion Function and Optimal Transport," On a Relation Between the RateDistortion Function and Optimal Transport, 2023. (Collaboration with Hamed Hassani, Foundations team.) [Link]
 X. Chen, H. Nikpey, J. Kim, S. Sarkar and S. SaeediBidokhti, "Containing a spread through sequential learning: to exploit or to explore?" preprint (arXiv:2303.00141), 2023.
 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. 233247, 2023. [Link]
 J. Cerviño, L. F. Chamon, B. D. Haeffele, R. Vidal and A. Ribeiro, "Learning globally smooth functions on manifolds," International Conference on Machine Learning, 2023, pp. 38153854. [Link]
 Q. Zhao, K. Lee, J. Liu, M. Huzaifa, X. Yu and T. Rosing, "FedHD: federated learning with hyperdimensional computing," Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, 2022, pp. 791793. [Link]
 X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peertopeer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172182, 2022. [Link]
 S. Shekhar and T. Javidi, "Instance Dependent Regret Analysis of Kernelized Bandits," International Conference on Machine Learning, 2022, pp. 1974719772. [Link]
 M. Lee, S. Shekhar and T. Javidi, "Multiscale zeroorder optimization of smooth functions in an RKHS," 2022 IEEE International Symposium on Information Theory (ISIT), 2022, pp. 288293. [Link]
 E. Ekaireb, X. Yu, K. Ergun, Q. Zhao, K. Lee, M. Huzaifa and T. Rosing, "ns3fl: Simulating Federated Learning with ns3," Proceedings of the 2022 Workshop on ns3, 2022, pp. 97104. [Link]
 R. Arghal, E. Lei and S. S. Bidokhti, "Robust graph neural networks via probabilistic lipschitz constraints," Learning for Dynamics and Control Conference, 2022, pp. 10731085. [Link]
Chip Design

 H. Chen, X. Zhang, K. Huang and F. Koushanfar, "AdaTest: Reinforcement learning and adaptive sampling for onchip hardware Trojan detection," ACM Transactions on Embedded Computing Systems, vol. 22, no. 2, pp. 123, 2023. [Link]
 A. B. Kahng, S. Thumathy and M. Woo, "An Effective CostSkew Tradeoff Heuristic for VLSI Global Routing," 2023 24th International Symposium on Quality Electronic Design (ISQED), 2023, pp. 18. [Link]
 C.K. Cheng, A. B. Kahng, S. Kundu, Y. Wang and Z. Wang, "Assessment of Reinforcement Learning for Macro Placement," Proceedings of the 2023 International Symposium on Physical Design, 2023, pp. 158166. (Invited paper.) [Link]
 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. 7075. [Link]
 A. B. Kahng, "Leveling up: A trajectory of OpenROAD, TILOS, and beyond," Proceedings of the 2022 International Symposium on Physical Design, 2022, pp. 7379. [Link]
 K. Zhu, H. Chen, M. Liu, X. Tang, W. Shi, N. Sun and D. Z. Pan, "Generativeadversarialnetworkguided wellaware placement for analog circuits," 2022 27th Asia and South Pacific Design Automation Conference (ASPDAC), 2022, pp. 519525. [Link]
 K. Zhu, H. Chen, M. Liu and D. Z. Pan, "Automating analog constraint extraction: From heuristics to learning," 2022 27th Asia and South Pacific Design Automation Conference (ASPDAC), 2022, pp. 108113. (Invited paper.) [Link]
 R. S. Rajarathnam, M. B. Alawieh, Z. Jiang, M. Iyer and D. Z. Pan, "DREAMPlaceFPGA: An opensource analytical placer for large scale heterogeneous FPGAs using deeplearning toolkit," 2022 27th Asia and South Pacific Design Automation Conference (ASPDAC), 2022, pp. 300306. [Link]
 A. B. Kahng, R. Varadarajan and Z. Wang, "RTLMP: Toward practical, humanquality chip planning and macro placement," Proceedings of the 2022 International Symposium on Physical Design, 2022, pp. 311. [Link]
 A. F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini, A. Goldie and D. Z. Pan, "Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives," 2022 27th Asia and South Pacific Design Automation Conference (ASPDAC), 2022, pp. 500505. (Invited paper.) [Link]
 K. Zhu, H. Chen, W. J. Turner, G. F. Kokai, P.H. Wei, D. Z. Pan and H. Ren, "TAG: Learning circuit spatial embedding from layouts," Proceedings of the 41st IEEE/ACM International Conference on ComputerAided Design, 2022, pp. 19. [Link]
 A. B. Kahng and Z. Wang, "ML for Design QoR Prediction," Machine Learning Applications in Electronic Design Automation, 2022, pp. 333. [Link]
 A. B. Kahng, "Machine Learning for CAD/EDA: The Road Ahead," IEEE Design and Test, vol. 40, no. 1, pp. 816, 2022. (Special issue on machine learning for CAD/EDA.) [Link]
 A. B. Kahng, "A Mixed OpenSource and Proprietary EDA Commons for Education and Prototyping," Proceedings of the 41st IEEE/ACM International Conference on ComputerAided Design, 2022, pp. 16. (Invited paper.) [Link]
 J. Jung, A. B. Kahng, R. Varadarajan and Z. Wang, "IEEE CEDA DATC: Expanding Research Foundations for IC Physical Design and MLEnabled EDA," Proceedings of the 41st IEEE/ACM International Conference on ComputerAided Design, 2022, pp. 18. (Invited paper.) [Link]
 Z. Jiang, M. Liu, Z. Guo, S. Zhang, Y. Lin and D. Pan, "A Tale of EDA's Long Tail: LongTailed Distribution Learning for Electronic Design Automation," Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022, pp. 135141. [Link]
 H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. S. Sapatnekar, Z. Wang, et al., "Physically Accurate Learningbased Performance Prediction of Hardwareaccelerated ML Algorithms," Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022, pp. 119126. [Link]
 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. 714. [Link]
 I. Bustany, A. B. Kahng, I. Koutis, B. Pramanik and Z. Wang, "SpecPart: A supervised spectral framework for hypergraph partitioning solution improvement," Proceedings of the 41st IEEE/ACM International Conference on ComputerAided Design, 2022, pp. 19. (Best paper award.) [Link]
 M. Rapp, H. Amrouch, Y. Lin, B. Yu, D. Z. Pan, M. Wolf and J. Henkel, "MLCAD: A survey of research in machine learning for CAD keynote paper," IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems, vol. 41, no. 10, pp. 31623181, 2021. (Keynote paper.) [Link]
 J. Jung, A. B. Kahng, S. Kim and R. Varadarajan, "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD ICCAD Special Session Paper," 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021, pp. 19. (Invited paper.) [Link]
 C.K. Cheng, A. B. Kahng, I. Kang, M. Kim, D. Lee, B. Lin, D. Park and M. Woo, "Coreeco: Concurrent refinement of detailed placeandroute for an efficient eco automation," 2021 IEEE 39th International Conference on Computer Design (ICCD), 2021, pp. 366373. [Link]
 J. Chen, I. H.R. Jiang, J. Jung, A. B. Kahng, S. Kim, V. N. Kravets, Y.L. Li, R. Varadarajan and M. Woo, "DATC RDF2021: Design flow and beyond iccad special session paper," 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021, pp. 16. (Invited paper.) [Link]
Robotics

 L. Zhou and V. Kumar, "Robust multirobot active target tracking against sensing and communication attacks," IEEE Transactions on Robotics, 2023. [Link]
 Z.H. Yin, B. Huang, Y. Qin, Q. Chen and X. Wang, "Rotating without Seeing: Towards Inhand Dexterity through Touch," preprint (arXiv:2303.10880), 2023.
 J. Ye, J. Wang, B. Huang, Y. Qin and X. Wang, "Learning continuous grasping function with a dexterous hand from human demonstrations," IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 28822889, 2023. [Link]
 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. 14301440. [Link]
 C. Yu, H. Yu and S. Gao, "Learning control admissibility models with graph neural networks for multiagent navigation," Conference on Robot Learning, 2023, pp. 934945. [Link]
 Y.H. Wu, J. Wang and X. Wang, "Learning generalizable dexterous manipulation from human grasp affordance," Conference on Robot Learning, 2023, pp. 618629. [Link]
 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, pp. 12351241. [Link]
 I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," preprint (arXiv:2305.18193), 2023.
 I. Spasojevic, X. Liu, A. Prabhu, A. Ribeiro, G. J. Pappas and V. Kumar, "Robust Localization of Aerial Vehicles via Active Control of Identical Ground Vehicles," preprint (arXiv:2308.06658), 2023.
 E. Sebastian, T. Duong, N. Atanasov, E. Montijano and C. Sagués, "LEMURS: Learning distributed multirobot interactions," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 77137719. [Link]
 Y. Qin, B. Huang, Z.H. Yin, H. Su and X. Wang, "DexPoint: Generalizable point cloud reinforcement learning for simtoreal dexterous manipulation," Conference on Robot Learning, 2023, pp. 594605. [Link]
 R. Patil, A. Langley and H. Christensen, "Scaling up multiagent patrolling in urban environments," Open Architecture/Open Business Model NetCentric Systems and Defense Transformation 2023, vol. 12544, pp. 91102, 2023. [Link]
 K. Mao, J. Welde, M. A. Hsieh and V. Kumar, "Trajectory Planning for the Bidirectional Quadrotor as a Differentially Flat Hybrid System," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 12421248. [Link]
 X. Liu, A. Prabhu, F. Cladera, I. D. Miller, L. Zhou, C. J. Taylor and V. Kumar, "Active metricsemantic mapping by multiple aerial robots," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 32823288. [Link]
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