TILOS PUBLICATIONS

Publications that acknowledge TILOS (NSF CCF-2112665) support

  1. D. Lim, J. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron, S. Jegelka, "Sign and Basis Invariant Networks for Spectral Graph Representation Learning," ICML workshop on Geometric and Topological Representation Learning, 2022. (in submission)
  2. B. Tahmasebi, D. Lim, S. Jegelka, "The Power of Recursion in Graph Neural Networks for Counting Substructures," ICML workshop on Topology, Algebra and Geometry in Data Science, 2022.
  3. C. Cai and Y. Wang, “Convergence of Invariant Graph Networks,” Intl. Conf. on Machine Learning (ICML), 2022.
  4. S. Jegelka, "Theory of Graph Neural Networks: Representation and Learning," Proc. of the Intl. Congress of Mathematicians (ICM), 2022.
  5. L. Zhou and V. Kumar, "Robust multi-robot active target tracking against sensing and communication attacks," American Control Conf. (ACC), 2022.
  6. H. Chen, X. Zhang, K. Huang and F. Koushanfar, “AdaTest: Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection,” ACM Transactions on Embedded Computing Systems (TECS), 2022.
  7. E. Ekaireb, X. Yu, K. Ergun, Q. Zhao, K. Lee, M. Huzaifa, and T. Rosing, “ns3-fl: Simulating Federated Learning with ns-3,” Workshop of NS3 (WNS3), 2022.
  8. L. Jarin-Lipschitz, X. Liu, Y. Tao and V. Kumar, "Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests," IEEE Intl. Conf. on Robotics and Automation, 2022.
  9. K. Sun, S. Chaves, P. Martin and V. Kumar, "RTGNN: A novel approach to model stochastic traffic dynamics," IEEE Intl. Conf. on Robotics and Automation, 2022.
  10. H. Sanghvi and C. J. Taylor, “Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models,” IEEE Intl. Conf. on Robotics and Automation, 2022.
  11. Z. Li, T. Duong and N. Atanasov, “Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics,” IEEE Open Journal of Control Systems (OJCSYS), 2022.
  12. Z. Li, T. Duong and N. Atanasov, “Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics,” Learning for Dynamics and Control (L4DC), 2022. (Link)
  13. X. Liu, G. V. Nardari, F. C. Ojeda, Y. Tao, A. Zhou, T. Donnelly, C. Qu, S. W. Chen, R. A. F. Romero, C. J. Taylor and V. Kumar, "Large-scale autonomous flight with real-time semantic slam under dense forest canopy," IEEE Robotics and Automation Letters 7(2) (2022), pp. 5512–5519.
  14. 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 7(2) (2022), pp. 5615–5622.
  15. D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations," IEEE Robotics and Automation Letters 7(2) (2022), pp. 5552–5559.
  16. C. Yun, S. Rajput and S. Sra, "Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond," Intl. Conf. on Learning Representations, 2022. (Link)
  17. K. Gatmiry, S. Jegelka, J. Kelner, "Optimization and Adaptive Generalization of Three layer Neural Networks," Intl. Conf. on Learning Representations, 2022. (Link)
  18. R. Yang, M. Zhang, N. Hansen, H. Xu and X. Wang, "Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers," Intl. Conf. on Learning Representations, 2022. (Link)
  19. A. B. Kahng, "Leveling Up: A Trajectory of OpenROAD, TILOS and Beyond," Proc. ACM/IEEE Intl. Symp. on Physical Design, 2022, pp. 73-79. (Link)
  20. A. B. Kahng, R. Varadarajan and Z. Wang, “RTL-MP: Toward Practical, Human-Quality Chip Planning and Macro Placement,” Proc. ACM/IEEE Intl. Symp. on Physical Design, 2022. (Link)
  21. T. Duong and N. Atanasov, “Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features,” IEEE Control Systems Letters (L-CSS), 2022. (Link)
  22. O. Mangoubi and N. K. Vishnoi, "Sampling from Log-Concave Distributions with Infinity-Distance Guarantees," arXiv preprint, 2022. (Link)
  23. A. B. Kahng, "Machine Learning for CAD/EDA: The Road Ahead," IEEE Design and Test (Special Issue on Machine Learning for CAD/EDA), to appear.
  24. R. Jangir, N. Hansen, S. Ghosal, M. Jain and X. Wang, "Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation," Robotics and Automation Letters (RA-L), 2022. (Link)
  25. J. Zhang, H. Li, S. Sra and A. Jadbabaie, "Rethinking Convergence in Deep Learning: Beyond Stationary Points," arXiv:2110.06256, 2022. (Preprint)
  26. A. Mehrotra, B. S. R. Pradelski and N. K. Vishnoi, "Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints," Computing Research Repository (CoRR), February 2022. (Link)
  27. R. Shen, L. Gao and Y. Ma, “On Optimal Early Stopping: Over-informative versus Under-informative Parametrization,” arXiv preprint arXiv:2202.09885, 2022. (Link)
  28. B. Li, Y. Ma, J. Kutz, and X. Yang, “The Adaptive Spectral Koopman Method for Dynamical Systems,” arXiv preprint arXiv:2202.09501, 2022. (Link)
  29. H. Hassani and A. Javanmard, “The curse of overparameterization in adversarial training: Precise analysis of robust generalization for random features regression,” arXiv preprint arXiv:2201.05149, 2022. (Link)
  30. 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 42(1) (2022), pp. 61–68.
  31. K. Zhu, H. Chen, M. Liu, X. Tang, W. Shi, N. Sun and D. Z. Pan, "Generative-Adversarial-Network-Guided Well-Aware Placement for Analog Circuits," IEEE/ACM Asian and South Pacific Design Automation Conf. (ASP-DAC), January 2022.
  32. 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," IEEE/ACM Asian and South Pacific Design Automation Conf. (ASP-DAC), January 2022.
  33. A. F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini and D. Z. Pan, “Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives,” IEEE/ACM Asia and South Pacific Design Automation Conf. (ASP-DAC), January, 2022. (Invited Paper)
  34. K. Zhu, H. Chen, M. Liu and D. Z. Pan, “Automating Analog Constraint Extraction: From Heuristics to Learning,” IEEE/ACM Asia and South Pacific Design Automation Conf. (ASP-DAC), January, 2022. (Invited Paper)
  35. 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 135 (2022), p. 109947.
  36. V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent First-Order Algorithm for Min-Max Optimization," axXiv preprint, 2021. (Link)
  37. E. McCarty, Q. Zhao, A. Sidiropoulos and Y. Wang, "NN-Baker: A neural-network infused algorithmic framework for optimization problems on geometric intersection graphs," Conf. on Neural Information Processing Systems, 2021, to appear.
  38. N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation," Conf. on Neural Information Processing Systems, 2021.
  39. M. Hao, Y. Li, Z. Di, N. B. Gundavarapu and X. Wang, "Test-Time Personalization with a Transformer for Human Pose Estimation," Conf. on Neural Information Processing Systems, 2021.
  40. J. Wang, H. Xu, M. Narasimhan and X. Wang, "Multi-Person 3D Motion Prediction with Multi-Range Transformers," Conf. on Neural Information Processing Systems, 2021.
  41. A. Jadbabaie, H. Mania, D. Shah and Suvrit Sra, "Time varying regression with hidden linear dynamics," arXiv:2112.14862. (Preprint) (Link)
  42. S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," arXiv:2112.00056. (Preprint) (Link)
  43. J. Chen, I. H.-R. Jiang, J. Jung, A. B. Kahng, S. Kim, V. N. Kravets, Y.-L. Li, R. Varadarajan and M. Woo, “DATC RDF-2021: Design Flow and Beyond,” Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design, 2021, pp. 1-6. (Invited Paper)
  44. J. Jung, A. B. Kahng, S. Kim and R. Varadarajan, "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD," Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design, 2021, pp. 1-9. (Invited Paper) (Link)
  45. C.-K. Cheng, A. B. Kahng, I. Kang, M. Kim, D. Lee, B. Lin, D. Park and M. Woo, "CoRe-ECO: Concurrent Refinement of Detailed Place-and-Route for an Efficient ECO Automation," Proc. ACM/IEEE Intl. Conf. on Computer Design, 2021, pp. 366-373. (Link)
  46. M. Rapp, H. Amrouch, Y. Lin, B. Yu, D. Z. Pan, M. Wolf and J. Henkel, "MLCAD: A Survey of Research in Machine Learning for CAD," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021. (accepted, Keynote Paper)
  47. Y. Freund, Y.-A. Ma and T. Zhang, "When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint," (preprint 2021, will acknowledge NSF CCF-2112665 in publication).

Additional Publications

  1. C. Liu, L. Zhu and M. Belkin, "Loss landscapes and optimization in over-parameterized non-linear systems and neural networks," Applied and Computational Harmonic Analysis, 2022. (Link)
  2. H. Chen, C. Fu, J. Zhao and F. Koushanfar, "GALU: A Genetic Algorithm Framework for Logic Unlocking," Digital Threats: Research and Practice, 2021.
  3. W. Mou, Y.-A. Ma, M. J. Wainwright, P. L. Bartlett and M. I. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm," Journal of Machine Learning Research (JMLR), 2021. (Link)
  4. V. Gandikota, A. Mazumdar and S. Pal, "Support Recovery of Sparse Signals from a Mixture of Linear Measurements," Conf. on Neural Information Processing Systems, 2021.
  5. J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka and S. Sra, "Can contrastive learning avoid shortcut solutions?" Conf. on Neural Information Processing Systems, 2021.
  6. A. Ghosh, R. K. Maity, S. Kadhe, A. Mazumdar and K. Ramchandran, "Communication-Efficient and Byzantine-Robust Distributed Learning With Error Feedback," IEEE Journal on Selected Areas in Information Theory, 2021, pp. 942-953.
  7. M. Belkin, "Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, Cambridge University Press, 2021, pp. 203-248. (Link)
  8. M. Shan, Q. Feng, Y. Jau and N. Atanasov, "ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description," IEEE/CVF Intl. Conf. on Computer Vision (ICCV), 2021. (Link)
  9. T. Duong and N. Atanasov, "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control," Robotics: Science and Systems (RSS), 2021. (Link)
  10. Y. Lin, Z. Jiang, J. Gu, W. Li, S. Dhar, H. Ren, B. Khailany and D. Z. Pan, "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021, pp. 748-761. (Link)