# TILOS PUBLICATIONS

### Publications that acknowledge TILOS (NSF CCF-2112665) support

- C. Yu, H. Yu and S. Gao, "Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation",
*CoRL (Conference on Robot Learning)*, 2022. - Y. Zhai and S. Gao, "Monte Carlo Tree Descent for Black-Box Optimization",
*NeurIPS*, 2022. - R. Zhang, C. Yu, J. Chen, C. Fan and S. Gao, "Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding",
*NeurIPS*, 2022. - E. Y. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems",
*NeurIPS*, 2022. - S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal Rates for Interactive Learning",
*NeurIPS*, 2022. - G. Velegkas, Z. Yang and A. Karbasi, "The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches",
*NeurIPS*, 2022. - W. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular Maximization in Clean Linear Time",
*NeurIPS*, 2022. - I. Mehalel, S. Hanneke, S. Moran, M. Mahmoody and A. Karbasi, "On Optimal Learning Under Targeted Data Poisoning",
*NeurIPS*, 2022. - A. Kalavasis, G. Velegkas and A. Karbasi, "Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes",
*NeurIPS*, 2022. - I. Han, A. Zandieh, J. Lee, R. Novak, L. Xiao and A. Karbasi, "Fast Neural Kernel Embeddings for General Activations",
*NeurIPS*, 2022. - K. E. Nikolakakis, F. Haddadpour, D. S. Kalogerias, A. Karbasi, "Black-Box Generalization",
*NeurIPS*, 2022. - I. Bustany, A. B. Kahng, Y. Koutis, B. Pramanik and Z. Wang, "SpecPart: A Supervised Spectral Framework for Hypergraph Partitioning Solution Improvement", (.pdf),
*Proc. ACM/IEEE International Conference on Computer-Aided Design*, 2022, to appear. - H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. S. Sapatnekar, Z. Wang and Z. Zeng, "Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms", (.pdf),
*Proc. ACM/IEEE Workshop on Machine Learning for CAD*, 2022. - V. A. Chhabria, W. Jiang, A. B. Kahng, S. S. Sapatnekar, "From Global Route to Detailed Route: ML for Fast and Accurate Wire Parasitics and Timing Prediction", (.pdf),
*Proc. ACM/IEEE Workshop on Machine Learning for CAD, 2022*, to appear. - X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-Peer Variational Federated Learning over Arbitrary Graphs,"
*to appear in IEEE Journal of Selected Areas in Information Theory*. - 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) - 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. - S. Shekar and T. Javidi, “Instance-dependent Regret Analysis of Kernelized Bandits,”
*Intl. Conf. on Machine Learning (ICML)*, 2022. - C. Cai and Y. Wang, “Convergence of Invariant Graph Networks,”
*Intl. Conf. on Machine Learning (ICML)*, 2022. - E. Lei, H. Hassani and S. S. Diokhti, “Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding,”
*Proc. of IEEE Int. Symp. on Information Theory (ISIT)*, 2022. - M. Lee, S. Shekar and T. Javidi, “Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS,”
*Proc. of IEEE Int. Symp. on Information Theory (ISIT)*, 2022. - S. Jegelka, "Theory of Graph Neural Networks: Representation and Learning,"
*Proc. of the Intl. Congress of Mathematicians (ICM)*, 2022. - V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent Min-Max Optimization Algorithm,"
*Proc. of Machine Learning Research*, 2022. (Link) - A. Mehrotra, B. S. Pradelski and N. K. Vishnoi, "Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints,"
*ACM Conf. on Fairness, Accountability, and Transparency*, June, 2022. (Link)) - O. Mangoubi, N. K. Vishnoi, "Faster Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk,"
*arxiv*, 2022. (Link) - L. Zhou and V. Kumar, “Robust multi-robot active target tracking against sensing and communication attacks,”
*American Control Conf. (ACC)*, 2022. - 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. - 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. - 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. - 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. - H. Sanghvi and C. J. Taylor, “Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models,”
*IEEE Intl. Conf. on Robotics and Automation*, 2022. - 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. - 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) - 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. - 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. - D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations,"
*IEEE Robotics and Automation Letters*7(2) (2022), pp. 5552–5559. - 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) - K. Gatmiry, S. Jegelka, J. Kelner, "Optimization and Adaptive Generalization of Three layer Neural Networks,"
*Intl. Conf. on Learning Representations*, 2022. (Link) - 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) - 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) - 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) - T. Duong and N. Atanasov, “Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features,”
*IEEE Control Systems Letters (L-CSS)*, 2022. (Link) - O. Mangoubi and N. K. Vishnoi, "Sampling from Log-Concave Distributions with Infinity-Distance Guarantees,"
*arXiv preprint,*2022. (Link) - 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. - 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) - J. Zhang, H. Li, S. Sra and A. Jadbabaie, "Rethinking Convergence in Deep Learning: Beyond Stationary Points,"
*arXiv:2110.06256*, 2022. (Preprint) - 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) - R. Shen, L. Gao and Y. Ma, “On Optimal Early Stopping: Over-informative versus Under-informative Parametrization,”
*arXiv preprint arXiv:2202.09885*, 2022. (Link) - B. Li, Y. Ma, J. Kutz, and X. Yang, “The Adaptive Spectral Koopman Method for Dynamical Systems,”
*arXiv preprint arXiv:2202.09501*, 2022. (Link) - 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) - 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. - 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. - 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. - 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) - 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) - 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. - 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) - 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. - 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. - 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. - 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. - A. Jadbabaie, H. Mania, D. Shah and Suvrit Sra, "Time varying regression with hidden linear dynamics,"
*arXiv:2112.14862*. (Preprint) (Link) - S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric,"
*arXiv:2112.00056*. (Preprint) (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,”
*Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design*, 2021, pp. 1-6. (Invited Paper) - 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) - 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) - 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) - 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

- 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) - H. Chen, C. Fu, J. Zhao and F. Koushanfar, "GALU: A Genetic Algorithm Framework for Logic Unlocking,"
*Digital Threats: Research and Practice*, 2021. - 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) - 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. - 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. - 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. - 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) - 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) - 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) - 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)