TILOS PUBLICATIONS
Publications that acknowledge TILOS (NSF CCF-2112665) support
- C.-K. Cheng, A. B. Kahng, S. Kundu, Y. Wang and Z. Wang, "Assessment of Reinforcement Learning for Macro Placement", (.pdf), Proc. ACM/IEEE Intl. Symp. on Physical Design, 2023, to appear. (Invited Paper) [arXiv]
- E. Sebastián, T. Duong, N. Atanasov, E. Montijano and C. Sagues, “LEMURS: Learning Distributed Multi-Robot Interactions”, IEEE Intl. Conf. on Robotics and Automation (ICRA), 2023.
- X. Cheng, J. Zhang, and S. Sra, “Theory and Algorithms for Diffusion Processes on Riemannian Manifolds”, arXiv preprint arXiv:2204.13665. (Link)
- J. Cervino, L. Ruiz and A. Ribeiro, “Learning by Transference: Training Graph Neural Networks on Growing Graphs”, IEEE Transactions on Signal Processing, 2023.
- J. Cervino, L. Chamon, B. D Haeffele, R. Vidal and A. Ribeiro, “Learning Globally Smooth Functions on Manifolds”, in submission.
- 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,” Intl. Conf. on Robotics and Automation, 2023, to appear.
- Y. Tao, Y. Wu, F. Cladera and V. Kumar, “SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain,” Intl. Conf. on Robotics and Automation, 2023, to appear.
- 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).
- B. Li, Y.-A. Ma, J. N. Kutz and X. Yang, "The Adaptive Spectral Koopman Method for Dynamical Systems", SIAM Journal on Applied Dynamical Systems, (to appear).
- A. Guelen, F. Memoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps", Intl. Symp. Comput. Geom. (SoCG), to appear, 2023.
- D. Lim, J. D. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron and S. Jegelka, "Sign and Basis Invariant Networks for Spectral Graph Representation Learning" Intl. Conf. on Learning Representations (ICLR), 2023. (Spotlight/notable-top-25%)
- X. Xia, G. Mishne, and Y. Wang, "Implicit Graphon Neural Representation", Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), to appear, 2023. (Oral Presentation) (Top 1.9% of submissions)
- B. Tahmasebi, D. Lim and S. Jegelka, "The Power of Recursion in Graph Neural Networks for Counting Substructures", Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), 2023. (Oral Presentation) (Top 1.9% of submissions)
- 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", ACM/IEEE Conf. on Internet of Things Design and Implementation (IoTDI), 2023.
- A. F. Budak, D. Smart, B. Swahn and D. Z. Pan, “APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors using DNN Learning,” Proc. IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC), January 2023.
- S. Kumar, J. Zamora, N. Hansen, R. Jangir and X. Wang, “Graph Inverse Reinforcement Learning from Diverse Videos”, Conf. on Robot Learning (CoRL), 2022 (Oral Presentation).
- Y. Wu, J. Wang and X. Wang, “Learning Generalizable Dexterous Manipulation from Human Grasp Affordance”, CoRL, 2022.
- Y. Qin, B. Huang, Z. Yin, H. Su and X. Wang, “Generalizable Point Cloud Policy Learning for Sim-to-Real Dexterous Manipulation”, CoRL, 2022.
- 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.
- M. Weber and S. Sra, “Computing Brascamp-Lieb Constants through the lens of Thompson Geometry,” arXiv preprint arXiv:2208.05013, (link).
- S. Sra and M. Weber, “On a class of geodesically convex optimization problems solved via Euclidean MM methods,” arXiv preprint arXiv:2206.11426, (link).
- O. Mangoubi and N. K. Vishnoi, “Re-Analyze Gauss: Bounds for private matrix approximation via Dyson Brownian motion,” Annual Conf. on Neural Information Processing Systems (NeurIPS), 2022. (Link).
- A. Mehrotra and N. K. Vishnoi, “Fair ranking with noisy protected attributes,” NeurIPS, 2022. (Link)
- O. Mangoubi and N. K. Vishnoi, “Sampling from log-concave distributions with infinity-distance guarantees,” NeurIPS, 2022. (Link)
- X. Cheng, J. Zhang, and S. Sra, “Efficient Sampling on Riemannian Manifolds via Langevin MCMC,” NeurIPS, 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.
- N. Chandramoorthy, A. Loukas, K. Gatmiry and S. Jegelka, “On the generalization of learning algorithms that do not converge,” NeurIPS, 2022.
- N. Karalias, J. Robinson, A. Loukas and S. Jegelka, “Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions,” NeurIPS, 2022.
- C.-Y. Chuang and S. Jegelka, “Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks”, 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.
- K. Zhu, H. Chen, W. Turner, G. Kokai, P. Wei, D. Z. Pan, and H. Ren, “TAG: Learning Circuit Spatial Embedding From Layouts,” Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022.
- J. Jung, A. B. Kahng, R. Varadarajan and Z. Wang, “IEEE CEDA DATC: Expanding Research Foundations for IC Physical Design and ML-Enabled EDA,” (.pdf), (.pptx), (.mp4), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022. (Invited Paper).
- A. B. Kahng, “A Mixed Open-Source and Proprietary EDA Commons for Education and Prototyping,”, (.pdf), (.pptx), (.mp4), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022. (Invited Paper).
- 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.
- Z. Jiang, M. Liu, Z. Guo, S. Zhang, Y. Lin, and D. Z. Pan, “A Tale of EDA’s Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation,” ACM/IEEE Workshop on Machine Learning for CAD, 2022.
- D. Wu, M. Chinazzi, A. Vespignani, Y.-A. Ma and R. Yu, "Multi-fidelity Hierarchical Neural Processes", KDD, 2022.
- 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. Chen, S. Lim, F. Memoli, Z. Wan and Y. Wang, “Weisfeiler-Lehman Meets Gromov-Wasserstein”, International Conference on Machine Learning (ICML), 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.
- J. Jin and S. Sra, “Understanding Riemannian Acceleration via a Proximal Extragradient Framework,” Proc. Conference on Learning Theory, July 2022, pp. 2924-2962.
- 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)) [Also: Computing Research Repository (CoRR), February 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)
- 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. Intl. Conf. on Machine Learning, 2022. (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. (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)