Publications by TILOS Faculty

Nikolay Atanasov

  • V. Duruisseaux, T. P. Duong, M. Leok and N. Atanasov, "Lie group forced variational integrator networks for learning and control of robot systems," Learning for Dynamics and Control Conference, 2023, pp. 731-744. [Link]
  • 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, pp. 7713-7719. [Link]
  • Z. Li, T. Duong and N. Atanasov, "Safe autonomous navigation for systems with learned SE (3) Hamiltonian dynamics," Learning for Dynamics and Control Conference, 2022, pp. 981-993. [Link]
  • Z. Li, T. Duong and N. Atanasov, "Robust and Safe Autonomous Navigation for Systems with Learned SE (3) Hamiltonian Dynamics," IEEE Open Journal of Control Systems, vol. 1, pp. 164-179, 2022. [Link]
  • T. Duong and N. Atanasov, "Adaptive control of SE (3) Hamiltonian dynamics with learned disturbance features," IEEE Control Systems Letters, vol. 6, pp. 2773-2778, 2022. [Link]
  • S. W. Chen, T. Wang, N. Atanasov, V. Kumar and M. Morari, "Large scale model predictive control with neural networks and primal active sets," Automatica, vol. 135, pp. 109947, 2022. [Link]
  • T. Duong and N. Atanasov, "Hamiltonian-based neural ODE networks on the SE (3) manifold for dynamics learning and control," arXiv preprint arXiv:2106.12782, 2021.
  • M. Shan, Q. Feng, Y.-Y. Jau and N. Atanasov, "ELLIPSDF: Joint object pose and shape optimization with a bi-level ellipsoid and signed distance function description," Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5946-5955. [Link]
  • P. Paritosh, N. Atanasov and S. Martinez, "Marginal density averaging for distributed node localization from local edge measurements," 2020 59th IEEE Conference on Decision and Control (CDC), 2020, pp. 2404-2410. [Link]
  • M. Shan, Q. Feng and N. Atanasov, "OrcVIO: Object residual constrained visual-inertial odometry," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 5104-5111. [Link]
  • E. Zobeidi, A. Koppel and N. Atanasov, "Dense incremental metric-semantic mapping via sparse gaussian process regression," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 6180-6187. [Link]
  • B. Schlotfeldt, D. Thakur, N. Atanasov, V. Kumar and G. J. Pappas, "Anytime planning for decentralized multirobot active information gathering," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1025-1032, 2018. [Link]
  • S. L. Bowman, N. Atanasov, K. Daniilidis and G. J. Pappas, "Probabilistic data association for semantic SLAM," 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 1722-1729. [Link]

Esmaeil Atashpaz-Gargari

  • E. Atashpaz-Gargari, M. S. Reis, U. M. Braga-Neto, J. Barrera and E. R. Dougherty, "A fast Branch-and-Bound algorithm for U-curve feature selection," Pattern Recognition, vol. 73, pp. 172-188, 2018. [Link]
  • E. Atashpaz-Gargari, "Smooth Optimal Control for a Class of Switched Systems Based on Fuzzy Theory and PSO," IOP Conference Series: Materials Science and Engineering, vol. 261, no. 1, pp. 012010, 2017. [Link]
  • E. Atashpaz-Gargari, U. M. Braga-Neto and E. R. Dougherty, "Improved branch-and-bound algorithm for U-curve optimization," 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, 2013, pp. 100-101. [Link]
  • E. Atashpaz-Gargari, R. Rajabioun, F. Hashemzadeh and F. Salmasi, "A decentralized PID controller based on optimal shrinkage of Gershgorin bands and PID tuning using colonial competitive algorithm," International Journal of Innovative Computing, Information and Control, vol. 5, no. 10, pp. 3227-3240, 2009.
  • E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition," 2007 IEEE Congress on Evolutionary Computation, 2007, pp. 4661-4667. [Link]

Mikhail Belkin

  • L. Hui, M. Belkin and S. Wright, "Cut your losses with squentropy," arXiv preprint arXiv:2302.03952, 2023.
  • A. Radhakrishnan, M. Belkin and C. Uhler, "Wide and deep neural networks achieve consistency for classification," Proceedings of the National Academy of Sciences, vol. 120, no. 14, pp. e2208779120, 2023. [Link]
  • 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]
  • 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]
  • D. Beaglehole, M. Belkin and P. Pandit, "Kernel Ridgeless Regression is Inconsistent for Low Dimensions," arXiv preprint arXiv:2205.13525, 2022.
  • C. Liu, L. Zhu and M. Belkin, "Loss landscapes and optimization in over-parameterized non-linear systems and neural networks," Applied and Computational Harmonic Analysis, vol. 59, pp. 85-116, 2022. [Link]
  • A. Radhakrishnan, G. Stefanakis, M. Belkin and C. Uhler, "Simple, fast, and flexible framework for matrix completion with infinite width neural networks," Proceedings of the National Academy of Sciences, vol. 119, no. 16, pp. e2115064119, 2022. [Link]
  • M. Belkin, "Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, vol. 30, pp. 203-248, 2021. [Link]
  • C. Liu, L. Zhu and M. Belkin, "Toward a theory of optimization for over-parameterized systems of non-linear equations: The lessons of deep learning," arXiv preprint arXiv:2003.00307, vol. 7, 2020.
  • C. Liu, L. Zhu and M. Belkin, "On the linearity of large non-linear models: When and why the tangent kernel is constant," Advances in Neural Information Processing Systems, vol. 33, pp. 15954-15964, 2020. [Link]
  • M. Belkin, D. Hsu, S. Ma and S. Mandal, "Reconciling modern machine-learning practice and the classical bias–variance trade-off," Proceedings of the National Academy of Sciences, vol. 116, no. 32, pp. 15849-15854, 2019. [Link]
  • J. Eldridge, M. Belkin and Y. Wang, "Unperturbed: Spectral analysis beyond Davis-Kahan," Algorithmic Learning Theory, 2018, pp. 321-358. [Link]
  • J. Eldridge, M. Belkin and Y. Wang, "Graphons, mergeons, and so on!" Advances in Neural Information Processing Systems, vol. 29, 2016. [Link]

Shirin Saeedi Bidokhti

  • X. Chen, H. Nikpey, J. Kim, S. Sarkar and S. Saeedi-Bidokhti, "Containing a spread through sequential learning: to exploit or to explore?" arXiv preprint arXiv:2303.00141, 2023.
  • 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]
  • E. Lei, H. Hassani and S. S. Bidokhti, "Neural estimation of the rate-distortion function with applications to operational source coding," IEEE Journal on Selected Areas in Information Theory, 2023. [Link]
  • E. Lei, H. Hassani and S. S. Bidokhti, "Federated Neural Compression Under Heterogeneous Data," arXiv preprint arXiv:2305.16416, 2023. (Collaboration with Hamed Hassani, Foundations team.)
  • R. Arghal, E. Lei and S. S. Bidokhti, "Robust graph neural networks via probabilistic lipschitz constraints," Learning for Dynamics and Control Conference, 2022, pp. 1073-1085. [Link]
  • X. Chen, X. Liao and S. S. Bidokhti, "Real-time sampling and estimation on random access channels: Age of information and beyond," IEEE INFOCOM 2021-IEEE Conference on Computer Communications, 2021, pp. 1-10. [Link]
  • S. S. Bidokhti, M. Wigger and R. Timo, "Noisy broadcast networks with receiver caching," IEEE Transactions on Information Theory, vol. 64, no. 11, pp. 6996-7016, 2018. [Link]
  • R. Timo, S. S. Bidokhti, M. Wigger and B. C. Geiger, "A rate-distortion approach to caching," IEEE Transactions on Information Theory, vol. 64, no. 3, pp. 1957-1976, 2017. [Link]
  • S. S. Bidokhti and G. Kramer, "Capacity bounds for diamond networks with an orthogonal broadcast channel," IEEE Transactions on Information Theory, vol. 62, no. 12, pp. 7103-7122, 2016. [Link]

Henrik I. Christensen

  • R. Patil, A. Langley and H. Christensen, "Scaling up multi-agent patrolling in urban environments," Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023, vol. 12544, pp. 91-102, 2023. [Link]
  • P. Parashar, A. K. Goel, B. Sheneman and H. I. Christensen, "Towards life-long adaptive agents: Using metareasoning for combining knowledge-based planning with situated learning," The Knowledge Engineering Review, vol. 33, pp. e24, 2018. [Link]
  • H. I. Christensen, A. Khan, S. Pokutta and P. Tetali, "Approximation and online algorithms for multidimensional bin packing: A survey," Computer Science Review, vol. 24, pp. 63-79, 2017. [Link]
  • T. Kunz, A. Thomaz and H. Christensen, "Hierarchical rejection sampling for informed kinodynamic planning in high-dimensional spaces," 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 89-96. [Link]
  • M. Dogar, R. A. Knepper, A. Spielberg, C. Choi, H. I. Christensen and D. Rus, "Multi-scale assembly with robot teams," The International Journal of Robotics Research, vol. 34, no. 13, pp. 1645-1659, 2015. [Link]
  • J. Folkesson and H. I. Christensen, "Graphical SLAM for outdoor applications," Journal of Field Robotics, vol. 24, no. 1-2, pp. 51-70, 2007. [Link]

Fan Chung Graham

  • F. C. Graham, "Regularity lemmas for clustering graphs," Advances in Applied Mathematics, vol. 126, pp. 101961, 2021. [Link]
  • F. C. Graham, R. Graham and S. Spiro, "Slow Fibonacci Walks," Journal of Number Theory, vol. 210, pp. 142-170, 2020. [Link]
  • F. C. Graham and J. Tobin, "The spectral gap of graphs arising from substring reversals," The Electronic Journal of Combinatorics, 2017, pp. P3-4. [Link]
  • S. Aksoy, F. C. Graham and X. Peng, "Extreme values of the stationary distribution of random walks on directed graphs," Advances in Applied Mathematics, vol. 81, pp. 128-155, 2016. [Link]
  • F. C. Graham, "A Brief Survey of PageRank Algorithms," IEEE Trans. Netw. Sci. Eng., vol. 1, no. 1, pp. 38-42, 2014. [Link]

Sicun Gao

  • 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]
  • 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]
  • R. Zhang, C. Yu, J. Chen, C. Fan and S. Gao, "Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding," Advances in Neural Information Processing Systems, vol. 35, pp. 30003-30015, 2022. [Link]
  • E. Y. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems," arXiv preprint arXiv:2210.12546, 2022.
  • Y.-C. Chang, N. Roohi and S. Gao, "Neural lyapunov control," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  • S. Kong, A. Solar-Lezama and S. Gao, "Delta-decision procedures for exists-forall problems over the reals," International Conference on Computer Aided Verification, 2018, pp. 219-235. [Link]
  • S. Gao, S. Kong and E. M. Clarke, "dReal: An SMT solver for nonlinear theories over the reals," Automated Deduction CADE-24: 24th International Conference on Automated Deduction, 2013, pp. 208-214. [Link]
  • S. Gao, J. Avigad and E. M. Clarke, "Delta-decidability over the reals," 2012 27th Annual IEEE Symposium on Logic in Computer Science, 2012, pp. 305-314. [Link]
  • S. Gao, J. Avigad and E. M. Clarke, "Delta-complete decision procedures for satisfiability over the reals," International Joint Conference on Automated Reasoning, 2012, pp. 286-300. [Link]

Hamed Hassani

  • 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. [Link]
  • E. Lei, H. Hassani and S. S. Bidokhti, "Neural estimation of the rate-distortion function with applications to operational source coding," IEEE Journal on Selected Areas in Information Theory, 2023. [Link]
  • E. Lei, H. Hassani and S. S. Bidokhti, "Federated Neural Compression Under Heterogeneous Data," arXiv preprint arXiv:2305.16416, 2023.
  • D. Lee, B. Moniri, X. Huang, E. Dobriban and H. Hassani, "Demystifying Disagreement-on-the-Line in High Dimensions," arXiv preprint arXiv:2301.13371, 2023.
  • A. Mitra, G. J. Pappas and H. Hassani, "Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning," arXiv preprint arXiv:2301.00944, 2023.
  • Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Self-consistency of the Fokker Planck equation," Conference on Learning Theory, 2022, pp. 817-841. [Link]
  • H. Hassani and A. Javanmard, "The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression," arXiv preprint arXiv:2201.05149, 2022.
  • A. Mokhtari, H. Hassani and A. Karbasi, "Stochastic conditional gradient methods: From convex minimization to submodular maximization," The Journal of Machine Learning Research, vol. 21, no. 1, pp. 4232-4280, 2020. [Link]
  • A. Fazeli, H. Hassani, M. Mondelli and A. Vardy, "Binary linear codes with optimal scaling: Polar codes with large kernels," IEEE Transactions on Information Theory, vol. 67, no. 9, pp. 5693-5710, 2020. [Link]
  • H. Hassani, A. Karbasi, A. Mokhtari and Z. Shen, "Stochastic conditional gradient++: (Non)convex minimization and continuous submodular maximization," SIAM Journal on Optimization, vol. 30, no. 4, pp. 3315-3344, 2020. [Link]
  • H. Hassani, S. Kudekar, O. Ordentlich, Y. Polyanskiy and R. Urbanke, "Almost optimal scaling of Reed-Muller codes on BEC and BSC channels," 2018 IEEE International Symposium on Information Theory (ISIT), 2018, pp. 311-315. [Link]
  • M. Mondelli, S. H. Hassani and R. L. Urbanke, "Unified scaling of polar codes: Error exponent, scaling exponent, moderate deviations, and error floors," IEEE Transactions on Information Theory, vol. 62, no. 12, pp. 6698-6712, 2016. [Link]
  • M. Mondelli, S. H. Hassani, I. Sason and R. L. Urbanke, "Achieving Marton’s region for broadcast channels using polar codes," IEEE Transactions on Information Theory, vol. 61, no. 2, pp. 783-800, 2014. [Link]

Tara Javidi

  • X. Zheng, T. Javidi and B. Touri, "Zeroth-Order Non-Convex Optimization for Cooperative Multi-Agent Systems with Diminishing Step Size and Smoothing Radius," IEEE Control Systems Letters, 2023. [Link]
  • V. Kungurtsev, M. Morafah, T. Javidi and G. Scutari, "Decentralized asynchronous non-convex stochastic optimization on directed graphs," IEEE Transactions on Control of Network Systems, 2023. [Link]
  • V. Kungurtsev, A. Cobb, T. Javidi and B. Jalaian, "Decentralized bayesian learning with metropolis-adjusted hamiltonian monte carlo," Machine Learning, 2023, pp. 1-29. [Link]
  • 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]
  • X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-peer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172-182, 2022. [Link]
  • S. Shekhar and T. Javidi, "Instance Dependent Regret Analysis of Kernelized Bandits," International Conference on Machine Learning, 2022, pp. 19747-19772. [Link]
  • M. Lee, S. Shekhar and T. Javidi, "Multi-scale zero-order optimization of smooth functions in an RKHS," 2022 IEEE International Symposium on Information Theory (ISIT), 2022, pp. 288-293. [Link]
  • M. Javaheripi, M. Samragh, T. Javidi and F. Koushanfar, "AdaNS: Adaptive non-uniform sampling for automated design of compact DNNs," IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 4, pp. 750-764, 2020. [Link]
  • M. J. Khojasteh, A. Khina, M. Franceschetti and T. Javidi, "Learning-based attacks in cyber-physical systems," IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 437-449, 2020. [Link]
  • B. D. Rouani, M. Samragh, T. Javidi and F. Koushanfar, "Safe machine learning and defeating adversarial attacks," IEEE Security & Privacy, vol. 17, no. 2, pp. 31-38, 2019. [Link]
  • A. Lalitha, N. Ronquillo and T. Javidi, "Improved target acquisition rates with feedback codes," IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 5, pp. 871-885, 2018. [Link]
  • M. Rao, A. Kipnis, T. Javidi, Y. C. Eldar and A. Goldsmith, "System identification from partial samples: Non-asymptotic analysis," 2016 IEEE 55th Conference on Decision and Control (CDC), 2016, pp. 2938-2944. [Link]
  • T. Javidi, Y. Kaspi and H. Tyagi, "Gaussian estimation under attack uncertainty," 2015 IEEE Information Theory Workshop (ITW), 2015, pp. 1-5. [Link]

Shatha Jawad

  • S. Jawad, R. P. Uhlig, P. P. Dey, M. N. Amin and B. Sinha, "Using Artificial Intelligence in Academia to Help Students Choose Their Engineering Program," 2023 ASEE Annual Conference & Exposition, 2023. [Link]
  • R. P. Uhlig, S. Jawad, B. Sinha, P. P. Dey and M. N. Amin, "Student Use of Artificial Intelligence to Write Technical Engineering Papers—Cheating or a Tool to Augment Learning," 2023 ASEE Annual Conference & Exposition, 2023. [Link]
  • S. K. Jawad, R. P. Uhlig, B. Sinha, M. N. Amin and P. P. Dey, "Multithread Affinity Scheduling Using a Decision Maker," Asian Journal of Computer and Information Systems, vol. 6, no. 4, 2018. [Link]
  • S. Jawad, "Design and evaluation of a neurofuzzy CPU scheduling algorithm," Proceedings of the 11th IEEE International Conference on Networking, Sensing, and Control, 2014, pp. 445-450. [Link]
  • S. K. Jawad, R. Rzouq, S. Hiary, S. Issa and A. Garageer, "A Design of Facial Recognition System Using Neural Network Based Geometrics 3D Facial," Proceedings of the 6th IASTED International Conference, vol. 643, no. 63, 2009. [Link]

Stefanie Jegelka

  • C.-Y. Chuang, S. Jegelka and D. Alvarez-Melis, "InfoOT: Information maximizing optimal transport," International Conference on Machine Learning, 2023, pp. 6228-6242. [Link]
  • S. Jegelka, "Theory of graph neural networks: Representation and learning," arXiv preprint arXiv:2204.07697, 2022.
  • N. Karalias, J. Robinson, A. Loukas and S. Jegelka, "Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions," Advances in Neural Information Processing Systems, vol. 35, pp. 15338-15352, 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. 34241-34257, 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," arXiv preprint arXiv:2202.13013, 2022. (Spotlight/notable top 25%)
  • C.-Y. Chuang and S. Jegelka, "Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks," Advances in Neural Information Processing Systems, vol. 35, pp. 2944-2957, 2022. [Link]
  • K. Gatmiry, S. Jegelka and J. Kelner, "Optimization and Adaptive Generalization of Three-layer Neural Networks," International Conference on Learning Representations, 2021. [Link]
  • J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka and S. Sra, "Can contrastive learning avoid shortcut solutions?" Advances in neural information processing systems, vol. 34, pp. 4974-4986, 2021. [Link]
  • V. Garg, S. Jegelka and T. Jaakkola, "Generalization and representational limits of graph neural networks," International Conference on Machine Learning, 2020, pp. 3419-3430. [Link]
  • K. Xu, J. Li, M. Zhang, S. S. Du, K.-i. Kawarabayashi and S. Jegelka, "What can neural networks reason about?" arXiv preprint arXiv:1905.13211, 2019.
  • K. Xu, W. Hu, J. Leskovec and S. Jegelka, "How powerful are graph neural networks?" arXiv preprint arXiv:1810.00826, 2018.
  • M. Staib and S. Jegelka, "Robust budget allocation via continuous submodular functions," International Conference on Machine Learning, 2017, pp. 3230-3240. [Link]
  • R. Iyer, S. Jegelka and J. Bilmes, "Fast semidifferential-based submodular function optimization," International Conference on Machine Learning, 2013, pp. 855-863. [Link]

Andrew B. Kahng

  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • A. B. Kahng, "Machine Learning for CAD/EDA: The Road Ahead," IEEE Design and Test, vol. 40, no. 1, pp. 8-16, 2022. (Special issue on machine learning for CAD/EDA.) [Link]
  • A. B. Kahng, "Leveling up: A trajectory of OpenROAD, TILOS, and beyond," Proceedings of the 2022 International Symposium on Physical Design, 2022, pp. 73-79. [Link]
  • A. B. Kahng, "A Mixed Open-Source and Proprietary EDA Commons for Education and Prototyping," Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022, pp. 1-6. (Invited paper.) [Link]
  • A. B. Kahng and Z. Wang, "ML for Design QoR Prediction," Machine Learning Applications in Electronic Design Automation, 2022, pp. 3-33. [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. 1-9. (Invited paper.) [Link]
  • J. Chen, I. H.-R. Jiang, J. Jung, A. B. Kahng, S. Kim, V. N. Kravets, Y.-L. Li, R. Varadarajan and M. Woo, "DATC RDF-2021: Design flow and beyond iccad special session paper," 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021, pp. 1-6. (Invited paper.) [Link]
  • C.-K. Cheng, A. B. Kahng, I. Kang, M. Kim, D. Lee, B. Lin, D. Park and M. Woo, "Core-eco: Concurrent refinement of detailed place-and-route for an efficient eco automation," 2021 IEEE 39th International Conference on Computer Design (ICCD), 2021, pp. 366-373. [Link]
  • A. B. Kahng, "Machine learning applications in physical design: Recent results and directions," Proceedings of the 2018 International Symposium on Physical Design, 2018, pp. 68-73. [Link]
  • W.-T. J. Chan, P.-H. Ho, A. B. Kahng and P. Saxena, "Routability optimization for industrial designs at sub-14nm process nodes using machine learning," Proceedings of the 2017 ACM on International Symposium on Physical Design, 2017, pp. 15-21. [Link]
  • C. J. Alpert, T. F. Chan, D. J.-H. Huang, A. B. Kahng, I. L. Markov, P. Mulet and K. Yan, "Faster minimization of linear wirelength for global placement," Proceedings of the 1997 International Symposium on Physical Design, 1997, pp. 4-11. [Link]
  • K. D. Boese, A. B. Kahng and S. Muddu, "A new adaptive multi-start technique for combinatorial global optimizations," Operations Research Letters, vol. 16, no. 2, pp. 101-113, 1994. [Link]
  • L. Hagen and A. B. Kahng, "New spectral methods for ratio cut partitioning and clustering," IEEE Transactions on CAD of Integrated Circuits and Systems, vol. 11, no. 9, pp. 1074-1085, 1992. [Link]

Amin Karbasi

  • A. Karbasi, N. L. Kuang, Y. Ma and S. Mitra, "Langevin Thompson sampling with logarithmic communication: bandits and reinforcement learning," International Conference on Machine Learning, 2023, pp. 15828-15860. [Link]
  • Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Self-consistency of the Fokker Planck equation," Conference on Learning Theory, 2022, pp. 817-841. [Link]
  • 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]
  • 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]
  • 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]
  • K. E. Nikolakakis, F. Haddadpour, A. Karbasi and D. S. Kalogerias, "Beyond lipschitz: Sharp generalization and excess risk bounds for full-batch gd," arXiv preprint arXiv:2204.12446, 2022.
  • J. Dadashkarimi, A. Karbasi and D. Scheinost, "Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport," International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022, pp. 386-395. [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. 8213-8229. [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. 35657-35671, 2022. [Link]
  • G. Velegkas, Z. Yang and A. Karbasi, "The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches," arXiv preprint arXiv:2203.01491, 2022.
  • A. Kalavasis, G. Velegkas and A. Karbasi, "Multiclass learnability beyond the pac framework: Universal rates and partial concept classes," Advances in Neural Information Processing Systems, vol. 35, pp. 20809-20822, 2022. [Link]
  • L. Chen, Q. Yu, H. Lawrence and A. Karbasi, "Minimax regret of switching-constrained online convex optimization: No phase transition," Advances in Neural Information Processing Systems, vol. 33, pp. 3477-3486, 2020. [Link]
  • E. Tohidi, R. Amiri, M. Coutino, D. Gesbert, G. Leus and A. Karbasi, "Submodularity in action: From machine learning to signal processing applications," IEEE Signal Processing Magazine, vol. 37, no. 5, pp. 120-133, 2020. [Link]
  • A. Mokhtari, H. Hassani and A. Karbasi, "Stochastic conditional gradient methods: From convex minimization to submodular maximization," The Journal of Machine Learning Research, vol. 21, no. 1, pp. 4232-4280, 2020. [Link]
  • H. Hassani, A. Karbasi, A. Mokhtari and Z. Shen, "Stochastic conditional gradient++: (Non)convex minimization and continuous submodular maximization," SIAM Journal on Optimization, vol. 30, no. 4, pp. 3315-3344, 2020. [Link]
  • B. Mirzasoleiman, A. Karbasi, R. Sarkar and A. Krause, "Distributed submodular maximization," The Journal of Machine Learning Research, vol. 17, no. 1, pp. 8330-8373, 2016. [Link]

Farinaz Koushanfar

  • 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]
  • X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-peer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172-182, 2022. [Link]
  • M. Javaheripi, M. Samragh, T. Javidi and F. Koushanfar, "AdaNS: Adaptive non-uniform sampling for automated design of compact DNNs," IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 4, pp. 750-764, 2020. [Link]
  • P. Neekhara, S. Hussain, P. Pandey, S. Dubnov, J. McAuley and F. Koushanfar, "Universal adversarial perturbations for speech recognition systems," arXiv preprint arXiv:1905.03828, 2019.
  • H. Chen, C. Fu, B. D. Rouhani, J. Zhao and F. Koushanfar, "DeepAttest: An end-to-end attestation framework for deep neural networks," Proceedings of the 46th International Symposium on Computer Architecture, 2019, pp. 487-498. [Link]
  • B. D. Rouani, M. Samragh, T. Javidi and F. Koushanfar, "Safe machine learning and defeating adversarial attacks," IEEE Security & Privacy, vol. 17, no. 2, pp. 31-38, 2019. [Link]
  • A. Mirhoseini, E. L. Dyer, E. M. Songhori, R. Baraniuk and F. Koushanfar, "RankMap: A framework for distributed learning from dense data sets," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 2717-2730, 2017. [Link]
  • B. D. Rouhani, A. Mirhoseini, E. M. Songhori and F. Koushanfar, "Automated real-time analysis of streaming big and dense data on reconfigurable platforms," ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 10, no. 1, pp. 1-22, 2016. [Link]

Vijay Kumar

  • Y. Tao, 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. 1235-1241. [Link]
  • X. Liu, A. Prabhu, F. Cladera, I. D. Miller, L. Zhou, C. J. Taylor and V. Kumar, "Active metric-semantic mapping by multiple aerial robots," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 3282-3288. [Link]
  • L. Zhou and V. Kumar, "Robust multi-robot active target tracking against sensing and communication attacks," IEEE Transactions on Robotics, 2023. [Link]
  • K. Mao, J. Welde, M. A. Hsieh and V. Kumar, "Trajectory Planning for the Bidirectional Quadrotor as a Differentially Flat Hybrid System," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 1242-1248. [Link]
  • I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," arXiv 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," arXiv preprint arXiv:2308.06658, 2023.
  • X. Liu, S. W. Chen, G. V. Nardari, C. Qu, F. C. Ojeda, C. J. Taylor and V. Kumar, "Challenges and opportunities for autonomous micro-UAVs in precision agriculture," IEEE Micro, vol. 42, no. 1, pp. 61-68, 2022. [Link]
  • S. W. Chen, T. Wang, N. Atanasov, V. Kumar and M. Morari, "Large scale model predictive control with neural networks and primal active sets," Automatica, vol. 135, pp. 109947, 2022. [Link]
  • S. Mayya, R. K. Ramachandran, L. Zhou, V. Senthil, D. Thakur, G. S. Sukhatme and V. Kumar, "Adaptive and risk-aware target tracking for robot teams with heterogeneous sensors," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5615-5622, 2022. [Link]
  • N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar and A. Rajeswaran, "MoDem: Accelerating visual model-based reinforcement learning with demonstrations," arXiv preprint arXiv:2212.05698, 2022.
  • L. Jarin-Lipschitz, X. Liu, Y. Tao and V. Kumar, "Experiments in adaptive replanning for fast autonomous flight in forests," 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 8185-8191. [Link]
  • K. Sun, S. Chaves, P. Martin and V. Kumar, "RTGNN: A novel approach to model stochastic traffic dynamics," 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 876-882. [Link]
  • D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5552-5559, 2022. [Link]
  • T. Nguyen, K. Mohta, C. J. Taylor and V. Kumar, "Vision-based multi-MAV localization with anonymous relative measurements using coupled probabilistic data association filter," 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 3349-3355. [Link]
  • E. Tolstaya, F. Gama, J. Paulos, G. Pappas, V. Kumar and A. Ribeiro, "Learning decentralized controllers for robot swarms with graph neural networks," Conference on Robot Learning, 2020, pp. 671-682. [Link]
  • M. Quigley, K. Mohta, S. S. Shivakumar, M. Watterson, Y. Mulgaonkar, M. Arguedas, K. Sun, S. Liu, B. Pfrommer, V. Kumar, et al., "The open vision computer: An integrated sensing and compute system for mobile robots," 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 1834-1840. [Link]
  • J. Paulos, S. W. Chen, D. Shishika and V. Kumar, "Decentralization of multiagent policies by learning what to communicate," 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 7990-7996. [Link]
  • X. Liu, S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das and V. Kumar, "Robust fruit counting: Combining deep learning, tracking, and structure from motion," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 1045-1052. [Link]
  • K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y. Mulgaonkar, C. J. Taylor and V. Kumar, "Robust stereo visual inertial odometry for fast autonomous flight," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 965-972, 2018. [Link]
  • B. Schlotfeldt, D. Thakur, N. Atanasov, V. Kumar and G. J. Pappas, "Anytime planning for decentralized multirobot active information gathering," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1025-1032, 2018. [Link]
  • S. W. Chen, S. S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C. J. Taylor and V. Kumar, "Counting apples and oranges with deep learning: A data-driven approach," IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 781-788, 2017. [Link]

Melvin Leok

  • W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M. E. Khan and M. Schmidt, "Simplifying Momentum-based Riemannian Submanifold Optimization," arXiv preprint arXiv:2302.09738, 2023.
  • V. Duruisseaux, T. P. Duong, M. Leok and N. Atanasov, "Lie group forced variational integrator networks for learning and control of robot systems," Learning for Dynamics and Control Conference, 2023, pp. 731-744. [Link]
  • V. Duruisseaux and M. Leok, "Practical perspectives on symplectic accelerated optimization," Optimization Methods and Software, 2023, pp. 1-39. [Link]
  • X. Shen and M. Leok, "Geometric exponential integrators," Journal of Computational Physics, vol. 382, pp. 27-42, 2019. [Link]
  • M. Leok, "Variational discretizations of gauge field theories using group-equivariant interpolation," Foundations of Computational Mathematics, vol. 19, pp. 965-989, 2019. [Link]
  • J. Hall and M. Leok, "Lie group spectral variational integrators," Foundations of Computational Mathematics, vol. 17, pp. 199-257, 2017. [Link]
  • H. Parks and M. Leok, "Variational integrators for interconnected Lagrange-Dirac systems," Journal of Nonlinear Science, vol. 27, pp. 1399-1434, 2017. [Link]
  • J. Vankerschaver, C. Liao and M. Leok, "Generating functionals and Lagrangian partial differential equations," Journal of Mathematical Physics, vol. 54, no. 8, 2013. [Link]

Yian Ma

  • 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]
  • D. Wu, R. Niu, M. Chinazzi, Y. Ma and R. Yu, "Disentangled Multi-Fidelity Deep Bayesian Active Learning," arXiv preprint arXiv:2305.04392, 2023.
  • B. Li, Y. Ma, J. N. Kutz and X. Yang, "The adaptive spectral Koopman method for dynamical systems," SIAM Journal on Applied Dynamical Systems, vol. 22, no. 3, pp. 1523-1551, 2023. [Link]
  • A. 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]
  • Y. Freund, Y.-A. Ma and T. Zhang, "When is the convergence time of Langevin algorithms dimension independent? A composite optimization viewpoint," The Journal of Machine Learning Research, vol. 23, no. 1, pp. 9604-9635, 2022. [Link]
  • R. Shen, L. Gao and Y.-A. Ma, "On Optimal Early Stopping: Over-informative versus Under-informative Parametrization," arXiv preprint arXiv:2202.09885, 2022.
  • D. Wu, M. Chinazzi, A. Vespignani, Y.-A. Ma and R. Yu, "Multi-fidelity hierarchical neural processes," Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 2029-2038. [Link]
  • W. Mou, Y.-A. Ma, M. J. Wainwright, P. L. Bartlett and M. I. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm," The Journal of Machine Learning Research, vol. 22, no. 1, pp. 1919-1959, 2021. [Link]
  • Y.-A. Ma, N. S. Chatterji, X. Cheng, N. Flammarion, P. L. Bartlett and M. I. Jordan, "Is there an analog of Nesterov acceleration for gradient-based MCMC?" Is there an analog of Nesterov acceleration for gradient-based MCMC?, 2021. [Link]
  • E. Mazumdar, A. Pacchiano, Y. Ma, P. L. Bartlett and M. I. Jordan, "On Thompson Sampling with Langevin Algorithms," arXiv preprint arXiv:2002.10002, 2020.
  • Y.-A. Ma, Y. Chen, C. Jin, N. Flammarion and M. I. Jordan, "Sampling can be faster than optimization," Proceedings of the National Academy of Sciences, vol. 116, no. 42, pp. 20881-20885, 2019. [Link]
  • N. Chatterji, N. Flammarion, Y. Ma, P. Bartlett and M. Jordan, "On the theory of variance reduction for stochastic gradient Monte Carlo," International Conference on Machine Learning, 2018, pp. 764-773. [Link]

Arya Mazumdar

  • X. Yu, L. Cherkasova, H. Vardhan, Q. Zhao, E. Ekaireb, X. Zhang, A. Mazumdar and T. Rosing, "Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks," Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, 2023, pp. 236-248. [Link]
  • H. Zhu, A. Ghosh and A. Mazumdar, "Optimal Compression of Unit Norm Vectors in the High Distortion Regime," 2023 IEEE International Symposium on Information Theory (ISIT), 2023, pp. 719-724. [Link]
  • A. Ghosh, A. Mazumdar, et al., "An Improved Algorithm for Clustered Federated Learning," arXiv preprint arXiv:2210.11538, 2022.
  • S. Pal, A. Mazumdar and V. Gandikota, "Support recovery of sparse signals from a mixture of linear measurements," Advances in Neural Information Processing Systems, vol. 34, pp. 19082-19094, 2021. [Link]
  • A. Ghosh, R. K. Maity, S. Kadhe, A. Mazumdar and K. Ramchandran, "Communication-efficient and byzantine-robust distributed learning with error feedback," IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 3, pp. 942-953, 2021. [Link]
  • V. Gandikota, A. Mazumdar and S. Pal, "Recovery of sparse linear classifiers from mixture of responses," Advances in Neural Information Processing Systems, vol. 33, pp. 14688-14698, 2020. [Link]
  • S. Pal and A. Mazumdar, "Recovery of sparse signals from a mixture of linear samples," International Conference on Machine Learning, 2020, pp. 7466-7475. [Link]
  • R. McKenna, R. K. Maity, A. Mazumdar and G. Miklau, "A workload-adaptive mechanism for linear queries under local differential privacy," arXiv preprint arXiv:2002.01582, 2020.
  • A. Ghosh, R. K. Maity and A. Mazumdar, "Distributed newton can communicate less and resist byzantine workers," Advances in Neural Information Processing Systems, vol. 33, pp. 18028-18038, 2020. [Link]
  • A. Agarwal, L. Flodin and A. Mazumdar, "Linear programming approximations for index coding," IEEE Transactions on Information Theory, vol. 65, no. 9, pp. 5547-5564, 2019. [Link]

David Pan

  • A. F. Budak, D. Smart, B. Swahn and D. Z. Pan, "APOSTLE: Asynchronously parallel optimization for sizing analog transistors using dnn learning," Proceedings of the 28th Asia and South Pacific Design Automation Conference, 2023, pp. 70-75. [Link]
  • Z. Jiang, M. Liu, Z. Guo, S. Zhang, Y. Lin and D. Pan, "A Tale of EDA's Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation," Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, 2022, pp. 135-141. [Link]
  • R. S. Rajarathnam, M. B. Alawieh, Z. Jiang, M. Iyer and D. Z. Pan, "DREAMPlaceFPGA: An open-source analytical placer for large scale heterogeneous FPGAs using deep-learning toolkit," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022, pp. 300-306. [Link]
  • K. Zhu, H. Chen, W. J. Turner, G. F. Kokai, P.-H. Wei, D. Z. Pan and H. Ren, "TAG: Learning circuit spatial embedding from layouts," Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022, pp. 1-9. [Link]
  • K. Zhu, H. Chen, M. Liu, X. Tang, W. Shi, N. Sun and D. Z. Pan, "Generative-adversarial-network-guided well-aware placement for analog circuits," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022, pp. 519-525. [Link]
  • K. Zhu, H. Chen, M. Liu and D. Z. Pan, "Automating analog constraint extraction: From heuristics to learning," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022, pp. 108-113. (Invited paper.) [Link]
  • A. F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini, A. Goldie and D. Z. Pan, "Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022, pp. 500-505. (Invited paper.) [Link]
  • M. Rapp, H. Amrouch, Y. Lin, B. Yu, D. Z. Pan, M. Wolf and J. Henkel, "MLCAD: A survey of research in machine learning for CAD keynote paper," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 10, pp. 3162-3181, 2021. (Keynote paper.) [Link]
  • M. B. Alawieh, Y. Lin, Z. Zhang, M. Li, Q. Huang and D. Z. Pan, "GAN-SRAF: Subresolution assist feature generation using generative adversarial networks," IEEE Transactions on CAD of Integrated Circuits and Systems, vol. 40, no. 2, pp. 373-385, 2020. [Link]
  • H. Chen, M. Liu, B. Xu, K. Zhu, X. Tang, S. Li, Y. Lin, N. Sun and D. Z. Pan, "MAGICAL: An OpenSource Fully Automated Analog IC Layout System from Netlist to GDSII," IEEE Design & Test, vol. 38, no. 2, pp. 19-26, 2020. [Link]
  • Y. Lin, S. Dhar, W. Li, H. Ren, B. Khailany and D. Z. Pan, "Dreamplace: Deep learning toolkit-enabled GPU acceleration for modern VSLI placement," Proceedings of the 56th Annual Design Automation Conference 2019, 2019, pp. 1-6. [Link]
  • W. Ye, M. B. Alawieh, Y. Lin and D. Z. Pan, "LithoGAN: End-to-end lithography modeling with generative adversarial networks," Proceedings of the 56th Annual Design Automation Conference 2019, 2019, pp. 1-6. [Link]
  • K. Zhu, M. Liu, Y. Lin, B. Xu, S. Li, X. Tang, N. Sun and D. Z. Pan, "GeniusRoute: A new analog routing paradigm using generative neural network guidance," 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019, pp. 1-8. [Link]

Jodi Reeves

  • B. D. Radhakrishnan, J. Reeves, J. J. Ninteman and C. Hahm, "Sustainability Intelligence: Emergence and Use of Big Data for Sustainable Urban Planning," 2016 ASEE Annual Conference & Exposition, 2016. [Link]
  • A. W. Lo, J. Reeves, P. Jenkins and R. Parkman, "Retention Initiatives for Working Adult Students in Accelerated Programs," Journal of Research in Innovative Teaching, vol. 9, no. 1, 2016. [Link]
  • B. Arnold and J. Reeves, "Translating best practices for student engagement to online STEAM courses," Proceedings of the 2014 American Society for Engineering Education Zone IV Conference, 2014. [Link]

Alejandro Ribeiro

  • J. Cerviño, L. Ruiz and A. Ribeiro, "Learning by Transference: Training Graph Neural Networks on Growing Graphs," IEEE Transactions on Signal Processing, vol. 71, pp. 233-247, 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. 3815-3854. [Link]
  • I. Spasojevic, X. Liu, A. Ribeiro, G. J. Pappas and V. Kumar, "Active Collaborative Localization in Heterogeneous Robot Teams," arXiv 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," arXiv preprint arXiv:2308.06658, 2023.
  • Z. Shen, Z. Wang, S. Kale, A. Ribeiro, A. Karbasi and H. Hassani, "Self-consistency of the Fokker Planck equation," Conference on Learning Theory, 2022, pp. 817-841. [Link]
  • D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5552-5559, 2022. [Link]
  • E. Tolstaya, F. Gama, J. Paulos, G. Pappas, V. Kumar and A. Ribeiro, "Learning decentralized controllers for robot swarms with graph neural networks," Conference on Robot Learning, 2020, pp. 671-682. [Link]
  • A. G. Marques, S. Segarra, G. Leus and A. Ribeiro, "Stationary graph processes and spectral estimation," IEEE Transactions on Signal Processing, vol. 65, no. 22, pp. 5911-5926, 2017. [Link]
  • S. Segarra, G. Mateos, A. G. Marques and A. Ribeiro, "Blind identification of graph filters," IEEE Transactions on Signal Processing, vol. 65, no. 5, pp. 1146-1159, 2016. [Link]
  • S. Segarra, A. G. Marques, G. Leus and A. Ribeiro, "Reconstruction of graph signals through percolation from seeding nodes," IEEE Transactions on Signal Processing, vol. 64, no. 16, pp. 4363-4378, 2016. [Link]
  • S. Segarra, A. G. Marques and A. Ribeiro, "Distributed linear network operators using graph filters," arXiv preprint arXiv:1510.03947, 2015.
  • A. G. Marques, S. Segarra, G. Leus and A. Ribeiro, "Sampling of graph signals with successive local aggregations," IEEE Transactions on Signal Processing, vol. 64, no. 7, pp. 1832-1843, 2015. [Link]

Tajana Rosing

  • X. Yu, 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]
  • 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]
  • 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]
  • 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]

Daniel Spielman

  • R. Kyng, Y. T. Lee, R. Peng, S. Sachdeva and D. A. Spielman, "Sparsified cholesky and multigrid solvers for connection laplacians," Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, 2016, pp. 842-850. [Link]
  • D. A. Spielman and S.-H. Teng, "Nearly linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems," SIAM Journal on Matrix Analysis and Applications, vol. 35, no. 3, pp. 835-885, 2014. [Link]
  • D. A. Spielman and S.-H. Teng, "A local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning," SIAM Journal on computing, vol. 42, no. 1, pp. 1-26, 2013. [Link]
  • P. Christiano, J. A. Kelner, A. Madry, D. A. Spielman and S.-H. Teng, "Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs," Proceedings of the forty-third annual ACM symposium on Theory of computing, 2011, pp. 273-282. [Link]
  • D. A. Spielman and N. Srivastava, "Graph sparsification by effective resistances," Proceedings of the 40th Annual ACM Symposium on Theory of Computing, 2008, pp. 563-568.

Suvrit Sra

  • Y. Tian, K. Zhang, R. Tedrake and S. Sra, "Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?" Learning for Dynamics and Control Conference, 2023, pp. 51-63. [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. 37787-37845. (Work with David Wu, co-supervised by the other authors.) [Link]
  • X. Cheng, J. Zhang and S. Sra, "Theory and Algorithms for Diffusion Processes on Riemannian Manifolds," arXiv 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. 5995-6006, 2022. [Link]
  • P. H. Zadeh and S. Sra, "Introducing discrepancy values of matrices with application to bounding norms of commutators," Linear Algebra and its Applications, vol. 651, pp. 359-384, 2022. (Work with PhD student Pourya Zadeh supervised by Sra.) [Link]
  • M. Weber and S. Sra, "On a class of geodesically convex optimization problems solved via Euclidean MM methods," arXiv preprint arXiv:2206.11426, 2022.
  • M. Weber and S. Sra, "Computing Brascamp-Lieb Constants through the lens of Thompson Geometry," arXiv preprint arXiv:2208.05013, 2022.
  • J. Jin and S. Sra, "Understanding Riemannian acceleration via a proximal extragradient framework," Conference on Learning Theory, 2022, pp. 2924-2962. [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," arXiv preprint arXiv:2202.13013, 2022. (Spotlight/notable top 25%)
  • S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," arXiv 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. 4974-4986, 2021. [Link]
  • C. Yun, S. Rajput and S. Sra, "Minibatch vs local SGD with shuffling: Tight convergence bounds and beyond," arXiv preprint arXiv:2110.10342, 2021.
  • A. Jadbabaie, H. Mania, D. Shah and S. Sra, "Time varying regression with hidden linear dynamics," arXiv preprint arXiv:2112.14862, 2021. (Work with postdoc Horia Mania co-supervised by the other named authors.)
  • K. Ahn and S. Sra, "From Nesterov’s estimate sequence to Riemannian acceleration," Conference on Learning Theory, 2020, pp. 84-118. [Link]
  • S. J. Reddi, A. Hefny, S. Sra, B. Poczos and A. Smola, "Stochastic variance reduction for nonconvex optimization," International Conference on Machine Learning, 2016, pp. 314-323. [Link]
  • H. Zhang, S. J Reddi and S. Sra, "Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds," Advances in Neural Information Processing Systems, vol. 29, 2016. [Link]
  • H. Zhang and S. Sra, "First-order methods for geodesically convex optimization," Conference on Learning Theory, 2016, pp. 1617-1638. [Link]
  • S. Sra and R. Hosseini, "Conic geometric optimization on the manifold of positive definite matrices," SIAM Journal on Optimization, vol. 25, no. 1, pp. 713-739, 2015. [Link]

Hao Su

  • Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "DexPoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation," Conference on Robot Learning, 2023, pp. 594-605. [Link]
  • S. Li, Z. Huang, T. Chen, T. Du, H. Su, J. B. Tenenbaum and C. Gan, "DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics," arXiv preprint arXiv:2304.03223, 2023.
  • J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, H. Su, Y. Tang, S. Tao, X. Wei, Y. Yao, et al., "ManiSkill2: A unified benchmark for generalizable manipulation skills," arXiv preprint arXiv:2302.04659, 2023.
  • Y. Xu, N. Hansen, Z. Wang, Y.-C. Chan, H. Su and Z. Tu, "On the feasibility of cross-task transfer with model-based reinforcement learning," arXiv preprint arXiv:2210.10763, 2022.
  • Y. Qin, H. Su and X. Wang, "From one hand to multiple hands: Imitation learning for dexterous manipulation from single-camera teleoperation," IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10873-10881, 2022. [Link]
  • Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation," Deep Reinforcement Learning Workshop NeurIPS 2022, 2022. [Link]
  • N. Hansen, Z. Yuan, Y. Ze, T. Mu, A. Rajeswaran, H. Su, H. Xu and X. Wang, "On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline," arXiv preprint arXiv:2212.05749, 2022.
  • N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar and A. Rajeswaran, "MoDem: Accelerating visual model-based reinforcement learning with demonstrations," arXiv preprint arXiv:2212.05698, 2022.
  • N. Hansen, X. Wang and H. Su, "Temporal difference learning for model predictive control," arXiv preprint arXiv:2203.04955, 2022.
  • J. Gu, D. S. Chaplot, H. Su and J. Malik, "Multi-skill mobile manipulation for object rearrangement," arXiv preprint arXiv:2209.02778, 2022. (Spotlight.)
  • N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation," Advances in Neural Information Processing Systems, vol. 34, pp. 3680-3693, 2021. [Link]
  • Z. Jia and H. Su, "Information-theoretic local minima characterization and regularization," International Conference on Machine Learning, 2020, pp. 4773-4783. [Link]
  • T. Mu, J. Gu, Z. Jia, H. Tang and H. Su, "Refactoring policy for compositional generalizability using self-supervised object proposals," Advances in Neural Information Processing Systems, vol. 33, pp. 8883-8894, 2020. [Link]
  • H. Tang, Z. Huang, J. Gu, B.-L. Lu and H. Su, "Towards scale-invariant graph-related problem solving by iterative homogeneous gnns," Advances in Neural Information Processing Systems, vol. 33, pp. 15811-15822, 2020. [Link]
  • Z. Huang, F. Liu and H. Su, "Mapping state space using landmarks for universal goal reaching," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  • C. R. Qi, H. Su, K. Mo and L. J. Guibas, "Pointnet: Deep learning on point sets for 3d classification and segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 652-660. [Link]

Camillo J. Taylor

  • X. Liu, A. Prabhu, F. Cladera, I. D. Miller, L. Zhou, C. J. Taylor and V. Kumar, "Active metric-semantic mapping by multiple aerial robots," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 3282-3288. [Link]
  • X. Liu, S. W. Chen, G. V. Nardari, C. Qu, F. C. Ojeda, C. J. Taylor and V. Kumar, "Challenges and opportunities for autonomous micro-UAVs in precision agriculture," IEEE Micro, vol. 42, no. 1, pp. 61-68, 2022. [Link]
  • 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]
  • H. Sanghvi and C. J. Taylor, "Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models," 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 7441-7447. [Link]
  • T. Nguyen, K. Mohta, C. J. Taylor and V. Kumar, "Vision-based multi-MAV localization with anonymous relative measurements using coupled probabilistic data association filter," 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 3349-3355. [Link]
  • X. Liu, S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das and V. Kumar, "Robust fruit counting: Combining deep learning, tracking, and structure from motion," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 1045-1052. [Link]
  • K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y. Mulgaonkar, C. J. Taylor and V. Kumar, "Robust stereo visual inertial odometry for fast autonomous flight," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 965-972, 2018. [Link]
  • S. W. Chen, S. S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C. J. Taylor and V. Kumar, "Counting apples and oranges with deep learning: A data-driven approach," IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 781-788, 2017. [Link]

Nisheeth Vishnoi

  • 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," arXiv preprint arXiv:2306.09835, 2023.
  • A. Mehrotra and N. K. Vishnoi, "Maximizing Submodular Functions for Recommendation in the Presence of Biases," Proceedings of the ACM Web Conference 2023, 2023, pp. 3625-3636. [Link]
  • 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]
  • 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]
  • O. Mangoubi and N. K. Vishnoi, "Faster Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk," arXiv preprint arXiv:2206.09384, 2022.
  • A. Mehrotra, B. S. Pradelski and N. K. Vishnoi, "Selection in the presence of implicit bias: the advantage of intersectional constraints," Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 599-609. [Link]
  • A. Mehrotra and N. Vishnoi, "Fair ranking with noisy protected attributes," Advances in Neural Information Processing Systems, vol. 35, pp. 31711-31725, 2022. [Link]
  • V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A convergent and dimension-independent min-max optimization algorithm," arXiv preprint arXiv:2006.12376, 2020.
  • J. Leake and N. K. Vishnoi, "On the computability of continuous maximum entropy distributions with applications," Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020, pp. 930-943. [Link]
  • O. Mangoubi and N. K. Vishnoi, "Nonconvex sampling with the Metropolis-adjusted Langevin algorithm," Conference on Learning Theory, 2019, pp. 2259-2293. [Link]
  • O. Mangoubi and N. K. Vishnoi, "Faster polytope rounding, sampling, and volume computation via a sub-linear ball walk," 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), 2019, pp. 1338-1357. [Link]
  • H. Lee, O. Mangoubi and N. Vishnoi, "Online sampling from log-concave distributions," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  • O. Mangoubi and N. Vishnoi, "Dimensionally tight bounds for second-order Hamiltonian Monte Carlo," Advances in Neural Information Processing Systems, vol. 31, 2018. [Link]

Xiaolong Wang

  • Z.-H. Yin, B. Huang, Y. Qin, Q. Chen and X. Wang, "Rotating without Seeing: Towards In-hand Dexterity through Touch," arXiv preprint arXiv:2303.10880, 2023.
  • Y.-H. Wu, J. Wang and X. Wang, "Learning generalizable dexterous manipulation from human grasp affordance," Conference on Robot Learning, 2023, pp. 618-629. [Link]
  • Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "DexPoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation," Conference on Robot Learning, 2023, pp. 594-605. [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. 1430-1440. [Link]
  • J. Ye, J. Wang, B. Huang, Y. Qin and X. Wang, "Learning continuous grasping function with a dexterous hand from human demonstrations," IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2882-2889, 2023. [Link]
  • Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu and X. Wang, "DexMV: Imitation learning for dexterous manipulation from human videos," European Conference on Computer Vision, 2022, pp. 570-587. [Link]
  • Y. Qin, H. Su and X. Wang, "From one hand to multiple hands: Imitation learning for dexterous manipulation from single-camera teleoperation," IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10873-10881, 2022. [Link]
  • Y. Qin, B. Huang, Z.-H. Yin, H. Su and X. Wang, "Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation," Deep Reinforcement Learning Workshop NeurIPS 2022, 2022. [Link]
  • X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-peer variational federated learning over arbitrary graphs," IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 2, pp. 172-182, 2022. [Link]
  • R. Jangir, N. Hansen, S. Ghosal, M. Jain and X. Wang, "Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3046-3053, 2022. [Link]
  • N. Hansen, Z. Yuan, Y. Ze, T. Mu, A. Rajeswaran, H. Su, H. Xu and X. Wang, "On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline," arXiv preprint arXiv:2212.05749, 2022.
  • N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar and A. Rajeswaran, "MoDem: Accelerating visual model-based reinforcement learning with demonstrations," arXiv preprint arXiv:2212.05698, 2022.
  • N. Hansen, X. Wang and H. Su, "Temporal difference learning for model predictive control," arXiv preprint arXiv:2203.04955, 2022.
  • C. S. Imai, M. Zhang, Y. Zhang, M. Kierebinski, R. Yang, Y. Qin and X. Wang, "Vision-guided quadrupedal locomotion in the wild with multi-modal delay randomization," 2022 IEEE/RSJ international conference on intelligent robots and systems (IROS), 2022, pp. 5556-5563. [Link]
  • Y. Li, M. Hao, Z. Di, N. B. Gundavarapu and X. Wang, "Test-time personalization with a transformer for human pose estimation," Advances in Neural Information Processing Systems, vol. 34, pp. 2583-2597, 2021. [Link]
  • R. Yang, M. Zhang, N. Hansen, H. Xu and X. Wang, "Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers," arXiv preprint arXiv:2107.03996, 2021.
  • N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation," Advances in Neural Information Processing Systems, vol. 34, pp. 3680-3693, 2021. [Link]
  • J. Wang, H. Xu, M. Narasimhan and X. Wang, "Multi-person 3D motion prediction with multi-range transformers," Advances in Neural Information Processing Systems, vol. 34, pp. 6036-6049, 2021. [Link]
  • R. Yang, H. Xu, Y. Wu and X. Wang, "Multi-task reinforcement learning with soft modularization," Advances in Neural Information Processing Systems, vol. 33, pp. 4767-4777, 2020. [Link]
  • Q. Long, Z. Zhou, A. Gupta, F. Fang, Y. Wu and X. Wang, "Evolutionary population curriculum for scaling multi-agent reinforcement learning," arXiv preprint arXiv:2003.10423, 2020.
  • W. Yang, X. Wang, A. Farhadi, A. Gupta and R. Mottaghi, "Visual semantic navigation using scene priors," arXiv preprint arXiv:1810.06543, 2018.
  • X. Wang, R. Girshick, A. Gupta and K. He, "Non-local neural networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7794-7803. [Link]
  • X. Wang and A. Gupta, "Videos as space-time region graphs," Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 399-417. [Link]

Yusu Wang

  • 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]
  • 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]
  • 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]
  • C. Cai, T. S. Hy, R. Yu and Y. Wang, "On the connection between MPNN and graph transformer," arXiv preprint arXiv:2301.11956, 2023.
  • A. B. Gülen, F. Mémoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps," arXiv preprint arXiv:2302.03771, 2023.
  • S. Chen, S. Lim, F. Mémoli, Z. Wan and Y. Wang, "Weisfeiler-Lehman meets Gromov-Wasserstein," International Conference on Machine Learning, 2022, pp. 3371-3416. [Link]
  • F. Mémoli, Z. Wan and Y. Wang, "Persistent Laplacians: Properties, algorithms and implications," SIAM Journal on Mathematics of Data Science, vol. 4, no. 2, pp. 858-884, 2022. [Link]
  • C. Cai and Y. Wang, "Convergence of invariant graph networks," International Conference on Machine Learning, 2022, pp. 2457-2484. [Link]
  • E. McCarty, Q. Zhao, A. Sidiropoulos and Y. Wang, "NN-Baker: A neural-network infused algorithmic framework for optimization problems on geometric intersection graphs," Advances in Neural Information Processing Systems, vol. 34, pp. 23023-23035, 2021. [Link]
  • Q. Zhao and Y. Wang, "Learning metrics for persistence-based summaries and applications for graph classification," Advances in Neural Information Processing Systems, vol. 32, 2019. [Link]
  • T. K. Dey, J. Wang and Y. Wang, "Graph reconstruction by discrete Morse theory," arXiv preprint arXiv:1803.05093, 2018.
  • J. Eldridge, M. Belkin and Y. Wang, "Unperturbed: Spectral analysis beyond Davis-Kahan," Algorithmic Learning Theory, 2018, pp. 321-358. [Link]
  • A. Sidiropoulos, D. Wang and Y. Wang, "Metric embeddings with outliers," Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms, 2017, pp. 670-689. [Link]
  • J. Eldridge, M. Belkin and Y. Wang, "Graphons, mergeons, and so on!" Advances in Neural Information Processing Systems, vol. 29, 2016. [Link]