Stephen Tu - RankMe

110 papers A* 20A 3C 3Misc 4Journal 63Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing. CoRR Zarif Ikram, Arad Firouzkouhi, Stephen Tu, Mahdi Soltanolkotabi, Paria Rashidinejad
2026	J	`jnl`	On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry. CoRR Mohammad Tinati, Stephen Tu
2026	J	`jnl`	Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons. CoRR Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sidhant Kaushik, Aditya Shah, Alex S. Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, Jesse Zhang
2025	J	`jnl`	CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations. CoRR Anthony Liang, Pavel Czempin, Matthew Hong, Yutai Zhou, Erdem Biyik, Stephen Tu
2025	—	`conf`	Data-Driven Reachability with Scenario Optimization and the Holdout Method. CDC Elizabeth Dietrich, Rosalyn A. Devonport, Stephen Tu, Murat Arcak
2025	J	`jnl`	Data-Driven Reachability with Scenario Optimization and the Holdout Method. CoRR Elizabeth Dietrich, Rosalyn A. Devonport, Stephen Tu, Murat Arcak
2025	—	`conf`	Incremental Composition of Learned Control Barrier Functions in Unknown Environments. CDC Paul Lutkus, Deepika Anantharaman, Stephen Tu, Lars Lindemann
2025	J	`jnl`	Integration Matters for Learning PDEs with Backwards SDEs. CoRR Sungje Park, Stephen Tu
2025	—	`conf`	Latent Representations for Control Design with Provable Stability and Safety Guarantees. CDC Paul Lutkus, Kaiyuan Wang, Lars Lindemann, Stephen Tu
2025	J	`jnl`	Latent Representations for Control Design with Provable Stability and Safety Guarantees. CoRR Paul Lutkus, Kaiyuan Wang, Lars Lindemann, Stephen Tu
2025	J	`jnl`	Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMs. CoRR Wei Yang, Jiacheng Pang, Shixuan Li, Paul Bogdan, Stephen Tu, Jesse Thomason
2025	J	`jnl`	Nearly Instance-Optimal Parameter Recovery from Many Trajectories via Hellinger Localization. CoRR Eliot Shekhtman, Yichen Zhou, Ingvar M. Ziemann, Nikolai Matni, Stephen Tu
2025	A*	`conf`	Shallow diffusion networks provably learn hidden low-dimensional structure. ICLR Nicholas Matthew Boffi, Arthur Jacot, Stephen Tu, Ingvar M. Ziemann
2024	J	`jnl`	Incremental Composition of Learned Control Barrier Functions in Unknown Environments. CoRR Paul Lutkus, Deepika Anantharaman, Stephen Tu, Lars Lindemann
2024	J	`jnl`	Learning from many trajectories. J. Mach. Learn. Res. Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
2024	J	`jnl`	Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models. Trans. Mach. Learn. Res. Sumeet Singh, Stephen Tu, Vikas Sindhwani
2024	J	`jnl`	Shallow diffusion networks provably learn hidden low-dimensional structure. CoRR Nicholas M. Boffi, Arthur Jacot, Stephen Tu, Ingvar M. Ziemann
2024	A*	`conf`	Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. ICML Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
2024	J	`jnl`	Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. CoRR Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
2024	J	`jnl`	Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem. CoRR Hesameddin Mohammadi, Mohammad Tinati, Stephen Tu, Mahdi Soltanolkotabi, Mihailo R. Jovanovic
2023	—	`conf`	Agile Catching with Whole-Body MPC and Blackbox Policy Learning. L4DC Saminda Abeyruwan, Alex Bewley, Nicholas Matthew Boffi, Krzysztof Marcin Choromanski, David B. D'Ambrosio, Deepali Jain, Pannag R. Sanketi, Anish Shankar, Vikas Sindhwani, Sumeet Singh, Jean-Jacques E. Slotine, Stephen Tu
2023	J	`jnl`	Agile Catching with Whole-Body MPC and Blackbox Policy Learning. CoRR Saminda Abeyruwan, Alex Bewley, Nicholas M. Boffi, Krzysztof Choromanski, David B. D'Ambrosio, Deepali Jain, Pannag Sanketi, Anish Shankar, Vikas Sindhwani, Sumeet Singh, Jean-Jacques E. Slotine, Stephen Tu
2023	A*	`conf`	Bootstrapped Representations in Reinforcement Learning. ICML Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	Bootstrapped Representations in Reinforcement Learning. CoRR Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	—	`conf`	Multi-Task Imitation Learning for Linear Dynamical Systems. L4DC Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni
2023	J	`jnl`	Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models. CoRR Sumeet Singh, Stephen Tu, Vikas Sindhwani
2023	Misc	`conf`	Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners. CoRL Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar
2023	J	`jnl`	Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners. CoRR Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar
2023	J	`jnl`	Safely Learning Dynamical Systems. CoRR Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
2023	A*	`conf`	The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. ICML Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
2023	J	`jnl`	The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. CoRR Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
2023	A*	`conf`	The noise level in linear regression with dependent data. NeurIPS Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
2023	J	`jnl`	The noise level in linear regression with dependent data. CoRR Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
2023	A*	`conf`	Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. ICRA David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley
2022	—	`conf`	Adversarially Robust Stability Certificates can be Sample-Efficient. L4DC Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni
2022	Misc	`conf`	Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRL Xuesu Xiao, Tingnan Zhang, Krzysztof Marcin Choromanski, Tsang-Wei Edward Lee, Anthony G. Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, Vikas Sindhwani
2022	J	`jnl`	Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRR Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski, Tsang-Wei Edward Lee, Anthony G. Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, Vikas Sindhwani
2022	J	`jnl`	Learning from many trajectories. CoRR Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
2022	A*	`conf`	Learning with little mixing. NeurIPS Ingvar M. Ziemann, Stephen Tu
2022	J	`jnl`	Learning with little mixing. CoRR Ingvar M. Ziemann, Stephen Tu
2022	J	`jnl`	Multi-Task Imitation Learning for Linear Dynamical Systems. CoRR Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni
2022	J	`jnl`	Nonparametric adaptive control and prediction: theory and randomized algorithms. J. Mach. Learn. Res. Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
2022	A	`conf`	On the Generalization of Representations in Reinforcement Learning. AISTATS Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
2022	J	`jnl`	On the Generalization of Representations in Reinforcement Learning. CoRR Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
2022	—	`conf`	On the Sample Complexity of Stability Constrained Imitation Learning. L4DC Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni
2022	A*	`conf`	TaSIL: Taylor Series Imitation Learning. NeurIPS Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni
2022	J	`jnl`	TaSIL: Taylor Series Imitation Learning. CoRR Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni
2022	A	`conf`	The role of optimization geometry in single neuron learning. AISTATS Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
2022	J	`jnl`	Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. CoRR David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley
2021	J	`jnl`	Adversarially Robust Stability Certificates can be Sample-Efficient. CoRR Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni
2021	J	`jnl`	Closing the Closed-Loop Distribution Shift in Safe Imitation Learning. CoRR Stephen Tu, Alexander Robey, Nikolai Matni
2021	—	`conf`	Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. ADHS Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni
2021	J	`jnl`	Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. CoRR Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni
2021	J	`jnl`	Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations. CoRR Lars Lindemann, Alexander Robey, Lejun Jiang, Stephen Tu, Nikolai Matni
2021	—	`conf`	Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms. CDC Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
2021	J	`jnl`	Random features for adaptive nonlinear control and prediction. CoRR Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
2021	—	`conf`	Regret Bounds for Adaptive Nonlinear Control. L4DC Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
2021	—	`conf`	Safely Learning Dynamical Systems from Short Trajectories. L4DC Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
2020	—	`conf`	Learning Control Barrier Functions from Expert Demonstrations. CDC Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
2020	J	`jnl`	Learning Control Barrier Functions from Expert Demonstrations. CoRR Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
2020	Misc	`conf`	Learning Hybrid Control Barrier Functions from Data. CoRL Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
2020	J	`jnl`	Learning Hybrid Control Barrier Functions from Data. CoRR Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
2020	Misc	`conf`	Learning Stability Certificates from Data. CoRL Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani
2020	J	`jnl`	Learning Stability Certificates from Data. CoRR Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani
2020	A*	`conf`	Observational Overfitting in Reinforcement Learning. ICLR Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur
2020	J	`jnl`	On the Sample Complexity of the Linear Quadratic Regulator. Found. Comput. Math. Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
2020	J	`jnl`	Regret Bounds for Adaptive Nonlinear Control. CoRR Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
2020	J	`jnl`	Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis. IEEE Trans. Control. Syst. Technol. Octavio Narvaez-Aroche, Pierre-Jean Meyer, Stephen Tu, Andrew K. Packard, Murat Arcak
2020	J	`jnl`	Safely Learning Dynamical Systems from Short Trajectories. CoRR Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
2019	—	`conf`	A Tutorial on Concentration Bounds for System Identification. CDC Nikolai Matni, Stephen Tu
2019	J	`jnl`	A Tutorial on Concentration Bounds for System Identification. CoRR Nikolai Matni, Stephen Tu
2019	A*	`conf`	Certainty Equivalence is Efficient for Linear Quadratic Control. NeurIPS Horia Mania, Stephen Tu, Benjamin Recht
2019	J	`jnl`	Certainty Equivalent Control of LQR is Efficient. CoRR Horia Mania, Stephen Tu, Benjamin Recht
2019	A*	`conf`	Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. NeurIPS Karl Krauth, Stephen Tu, Benjamin Recht
2019	J	`jnl`	Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. CoRR Karl Krauth, Stephen Tu, Benjamin Recht
2019	—	`conf`	From self-tuning regulators to reinforcement learning and back again. CDC Nikolai Matni, Alexandre Proutière, Anders Rantzer, Stephen Tu
2019	J	`jnl`	From self-tuning regulators to reinforcement learning and back again. CoRR Nikolai Matni, Alexandre Proutière, Anders Rantzer, Stephen Tu
2019	C	`conf`	Minimax Lower Bounds for H∞-Norm Estimation. ACC Stephen Tu, Ross Boczar, Benjamin Recht
2019	J	`jnl`	Observational Overfitting in Reinforcement Learning. CoRR Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur
2019	C	`conf`	Safely Learning to Control the Constrained Linear Quadratic Regulator. ACC Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht
2019	—	`—`	Sample Complexity Bounds for the Linear Quadratic Regulator. Stephen Tu
2019	A*	`conf`	The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint. COLT Stephen Tu, Benjamin Recht
2018	J	`jnl`	Learning Contracting Vector Fields For Stable Imitation Learning. CoRR Vikas Sindhwani, Stephen Tu, Mohi Khansari
2018	A*	`conf`	Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. COLT Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
2018	J	`jnl`	Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. CoRR Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
2018	A*	`conf`	Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. ICML Stephen Tu, Benjamin Recht
2018	J	`jnl`	Minimax Lower Bounds for ℋ CoRR Stephen Tu, Ross Boczar, Benjamin Recht
2018	C	`conf`	On the Approximation of Toeplitz Operators for Nonparametric H ACC Stephen Tu, Ross Boczar, Benjamin Recht
2018	A*	`conf`	Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. NeurIPS Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
2018	J	`jnl`	Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. CoRR Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
2018	J	`jnl`	Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis. CoRR Octavio Narvaez-Aroche, Pierre-Jean Meyer, Stephen Tu, Andrew K. Packard, Murat Arcak
2018	J	`jnl`	Safely Learning to Control the Constrained Linear Quadratic Regulator. CoRR Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht
2018	J	`jnl`	The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint. CoRR Stephen Tu, Benjamin Recht
2017	A*	`conf`	Breaking Locality Accelerates Block Gauss-Seidel. ICML Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht
2017	J	`jnl`	Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. CoRR Stephen Tu, Benjamin Recht
2017	J	`jnl`	Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification. CoRR Stephen Tu, Ross Boczar, Andrew K. Packard, Benjamin Recht
2017	J	`jnl`	On the Approximation of Toeplitz Operators for Nonparametric $\mathcal{H}_\infty$-norm Estimation. CoRR Stephen Tu, Ross Boczar, Benjamin Recht
2017	J	`jnl`	On the Sample Complexity of the Linear Quadratic Regulator. CoRR Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
2016	J	`jnl`	CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht
2016	J	`jnl`	Large Scale Kernel Learning using Block Coordinate Descent. CoRR Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht
2016	A*	`conf`	Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. ICML Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht
2015	A*	`conf`	Machine Learning Classification over Encrypted Data. NDSS Raphael Bost, Raluca Ada Popa, Stephen Tu, Shafi Goldwasser
2014	A*	`conf`	Fast Databases with Fast Durability and Recovery Through Multicore Parallelism. OSDI Wenting Zheng, Stephen Tu, Eddie Kohler, Barbara Liskov
2014	J	`jnl`	Machine Learning Classification over Encrypted Data. IACR Cryptol. ePrint Arch. Raphael Bost, Raluca Ada Popa, Stephen Tu, Shafi Goldwasser
2013	J	`jnl`	Anti-Caching: A New Approach to Database Management System Architecture. Proc. VLDB Endow. Justin A. DeBrabant, Andrew Pavlo, Stephen Tu, Michael Stonebraker, Stanley B. Zdonik
2013	J	`jnl`	Processing Analytical Queries over Encrypted Data. Proc. VLDB Endow. Stephen Tu, M. Frans Kaashoek, Samuel Madden, Nickolai Zeldovich
2013	A*	`conf`	Speedy transactions in multicore in-memory databases. SOSP Stephen Tu, Wenting Zheng, Eddie Kohler, Barbara Liskov, Samuel Madden
2012	A	`conf`	The HipHop compiler for PHP. OOPSLA Haiping Zhao, Iain Proctor, Minghui Yang, Xin Qi, Mark Williams, Qi Gao, Guilherme Ottoni, Andrew Paroski, Scott MacVicar, Jason Evans, Stephen Tu
2010	—	`conf`	PIQL: a performance insightful query language. SIGMOD Conference Michael Armbrust, Stephen Tu, Armando Fox, Michael J. Franklin, David A. Patterson, Nick Lanham, Beth Trushkowsky, Jesse Trutna
2010	—	`conf`	The case for PIQL: a performance insightful query language. SoCC Michael Armbrust, Nick Lanham, Stephen Tu, Armando Fox, Michael J. Franklin, David A. Patterson