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Stephen Tu
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2020 – today
- 2024
- [j6]Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi:
Learning from many trajectories. J. Mach. Learn. Res. 25: 216:1-216:109 (2024) - [c42]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. ICML 2024 - [i46]Ingvar M. Ziemann, Stephen Tu, George J. Pappas
, Nikolai Matni:
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. CoRR abs/2402.05928 (2024) - [i45]Paul Lutkus, Deepika Anantharaman, Stephen Tu, Lars Lindemann:
Incremental Composition of Learned Control Barrier Functions in Unknown Environments. CoRR abs/2409.12382 (2024) - [i44]Nicholas M. Boffi, Arthur Jacot, Stephen Tu, Ingvar M. Ziemann:
Shallow diffusion networks provably learn hidden low-dimensional structure. CoRR abs/2410.11275 (2024) - 2023
- [c41]Allen Z. Ren, Anushri Dixit
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners. CoRL 2023: 661-682 - [c40]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. ICML 2023: 18686-18713 - [c39]Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu:
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. ICML 2023: 27737-27821 - [c38]David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley:
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. ICRA 2023: 11336-11342 - [c37]Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni:
Multi-Task Imitation Learning for Linear Dynamical Systems. L4DC 2023: 586-599 - [c36]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:
Agile Catching with Whole-Body MPC and Blackbox Policy Learning. L4DC 2023: 851-863 - [c35]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
The noise level in linear regression with dependent data. NeurIPS 2023 - [i43]Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu:
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. CoRR abs/2305.09619 (2023) - [i42]Ingvar M. Ziemann, Stephen Tu, George J. Pappas
The noise level in linear regression with dependent data. CoRR abs/2305.11165 (2023) - [i41]Amir Ali Ahmadi
Safely Learning Dynamical Systems. CoRR abs/2305.12284 (2023) - [i40]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:
Agile Catching with Whole-Body MPC and Blackbox Policy Learning. CoRR abs/2306.08205 (2023) - [i39]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. CoRR abs/2306.10171 (2023) - [i38]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:
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners. CoRR abs/2307.01928 (2023) - [i37]Sumeet Singh, Stephen Tu, Vikas Sindhwani:
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models. CoRR abs/2309.05803 (2023) - 2022
- [j5]Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
Nonparametric adaptive control and prediction: theory and randomized algorithms. J. Mach. Learn. Res. 23: 281:1-281:46 (2022) - [c34]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. AISTATS 2022: 4132-4157 - [c33]Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
The role of optimization geometry in single neuron learning. AISTATS 2022: 11528-11549 - [c32]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:
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRL 2022: 1708-1721 - [c31]Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni:
On the Sample Complexity of Stability Constrained Imitation Learning. L4DC 2022: 180-191 - [c30]Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni:
Adversarially Robust Stability Certificates can be Sample-Efficient. L4DC 2022: 532-545 - [c29]Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni:
TaSIL: Taylor Series Imitation Learning. NeurIPS 2022 - [c28]Ingvar M. Ziemann, Stephen Tu:
Learning with little mixing. NeurIPS 2022 - [i36]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. CoRR abs/2203.00543 (2022) - [i35]Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
Learning from many trajectories. CoRR abs/2203.17193 (2022) - [i34]Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni:
TaSIL: Taylor Series Imitation Learning. CoRR abs/2205.14812 (2022) - [i33]Ingvar M. Ziemann, Stephen Tu:
Learning with little mixing. CoRR abs/2206.08269 (2022) - [i32]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:
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRR abs/2209.10780 (2022) - [i31]David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley:
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. CoRR abs/2210.02343 (2022) - [i30]Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni:
Multi-Task Imitation Learning for Linear Dynamical Systems. CoRR abs/2212.00186 (2022) - 2021
- [c27]Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni:
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. ADHS 2021: 1-6 - [c26]Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms. CDC 2021: 2935-2942 - [c25]Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
Regret Bounds for Adaptive Nonlinear Control. L4DC 2021: 471-483 - [c24]Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu:
Safely Learning Dynamical Systems from Short Trajectories. L4DC 2021: 498-509 - [i29]Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni:
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. CoRR abs/2101.06492 (2021) - [i28]Stephen Tu, Alexander Robey, Nikolai Matni:
Closing the Closed-Loop Distribution Shift in Safe Imitation Learning. CoRR abs/2102.09161 (2021) - [i27]Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
Random features for adaptive nonlinear control and prediction. CoRR abs/2106.03589 (2021) - [i26]Lars Lindemann, Alexander Robey, Lejun Jiang, Stephen Tu, Nikolai Matni:
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations. CoRR abs/2111.09971 (2021) - [i25]Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni:
Adversarially Robust Stability Certificates can be Sample-Efficient. CoRR abs/2112.10690 (2021) - 2020
- [j4]Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu:
On the Sample Complexity of the Linear Quadratic Regulator. Found. Comput. Math. 20(4): 633-679 (2020) - [j3]Octavio Narvaez-Aroche
Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis. IEEE Trans. Control. Syst. Technol. 28(6): 2390-2403 (2020) - [c23]Alexander Robey, Haimin Hu
Learning Control Barrier Functions from Expert Demonstrations. CDC 2020: 3717-3724 - [c22]Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani:
Learning Stability Certificates from Data. CoRL 2020: 1341-1350 - [c21]Lars Lindemann, Haimin Hu
Learning Hybrid Control Barrier Functions from Data. CoRL 2020: 1351-1370 - [c20]Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur:
Observational Overfitting in Reinforcement Learning. ICLR 2020 - [i24]Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni:
Learning Control Barrier Functions from Expert Demonstrations. CoRR abs/2004.03315 (2020) - [i23]Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani:
Learning Stability Certificates from Data. CoRR abs/2008.05952 (2020) - [i22]Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni:
Learning Hybrid Control Barrier Functions from Data. CoRR abs/2011.04112 (2020) - [i21]Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu:
Safely Learning Dynamical Systems from Short Trajectories. CoRR abs/2011.12257 (2020) - [i20]Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
Regret Bounds for Adaptive Nonlinear Control. CoRR abs/2011.13101 (2020)
2010 – 2019
- 2019
- [b1]Stephen Tu:
Sample Complexity Bounds for the Linear Quadratic Regulator. University of California, Berkeley, USA, 2019 - [c19]Stephen Tu, Ross Boczar, Benjamin Recht:
Minimax Lower Bounds for H∞-Norm Estimation. ACC 2019: 3538-3543 - [c18]Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht:
Safely Learning to Control the Constrained Linear Quadratic Regulator. ACC 2019: 5582-5588 - [c17]Nikolai Matni, Alexandre Proutière, Anders Rantzer, Stephen Tu:
From self-tuning regulators to reinforcement learning and back again. CDC 2019: 3724-3740 - [c16]Nikolai Matni, Stephen Tu:
A Tutorial on Concentration Bounds for System Identification. CDC 2019: 3741-3749 - [c15]Stephen Tu, Benjamin Recht:
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint. COLT 2019: 3036-3083 - [c14]Karl Krauth, Stephen Tu, Benjamin Recht:
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. NeurIPS 2019: 8512-8522 - [c13]Horia Mania, Stephen Tu, Benjamin Recht:
Certainty Equivalence is Efficient for Linear Quadratic Control. NeurIPS 2019: 10154-10164 - [i19]Horia Mania, Stephen Tu, Benjamin Recht:
Certainty Equivalent Control of LQR is Efficient. CoRR abs/1902.07826 (2019) - [i18]Karl Krauth, Stephen Tu, Benjamin Recht:
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. CoRR abs/1905.12842 (2019) - [i17]Nikolai Matni, Alexandre Proutière, Anders Rantzer, Stephen Tu:
From self-tuning regulators to reinforcement learning and back again. CoRR abs/1906.11392 (2019) - [i16]Nikolai Matni, Stephen Tu:
A Tutorial on Concentration Bounds for System Identification. CoRR abs/1906.11395 (2019) - [i15]Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur:
Observational Overfitting in Reinforcement Learning. CoRR abs/1912.02975 (2019) - 2018
- [c12]Stephen Tu, Ross Boczar, Benjamin Recht:
On the Approximation of Toeplitz Operators for Nonparametric H∞-norm Estimation. ACC 2018: 1867-1872 - [c11]Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht:
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. COLT 2018: 439-473 - [c10]Stephen Tu, Benjamin Recht:
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. ICML 2018: 5012-5021 - [c9]Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu:
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. NeurIPS 2018: 4192-4201 - [i14]Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht:
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. CoRR abs/1802.08334 (2018) - [i13]Vikas Sindhwani, Stephen Tu, Mohi Khansari:
Learning Contracting Vector Fields For Stable Imitation Learning. CoRR abs/1804.04878 (2018) - [i12]Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu:
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. CoRR abs/1805.09388 (2018) - [i11]Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht:
Safely Learning to Control the Constrained Linear Quadratic Regulator. CoRR abs/1809.10121 (2018) - [i10]Stephen Tu, Ross Boczar, Benjamin Recht:
Minimax Lower Bounds for ℋ∞-Norm Estimation. CoRR abs/1809.10855 (2018) - [i9]Octavio Narvaez-Aroche
Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis. CoRR abs/1811.07011 (2018) - [i8]Stephen Tu, Benjamin Recht:
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint. CoRR abs/1812.03565 (2018) - 2017
- [c8]Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht:
Breaking Locality Accelerates Block Gauss-Seidel. ICML 2017: 3482-3491 - [i7]Stephen Tu, Ross Boczar, Andrew K. Packard, Benjamin Recht:
Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification. CoRR abs/1707.04791 (2017) - [i6]Stephen Tu, Ross Boczar, Benjamin Recht:
On the Approximation of Toeplitz Operators for Nonparametric $\mathcal{H}_\infty$-norm Estimation. CoRR abs/1709.10203 (2017) - [i5]Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu:
On the Sample Complexity of the Linear Quadratic Regulator. CoRR abs/1710.01688 (2017) - [i4]Stephen Tu, Benjamin Recht:
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. CoRR abs/1712.08642 (2017) - 2016
- [c7]Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. ICML 2016: 964-973 - [i3]Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht:
Large Scale Kernel Learning using Block Coordinate Descent. CoRR abs/1602.05310 (2016) - [i2]Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht:
CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR abs/1605.09721 (2016) - 2015
- [c6]Raphael Bost, Raluca Ada Popa, Stephen Tu, Shafi Goldwasser:
Machine Learning Classification over Encrypted Data. NDSS 2015 - 2014
- [c5]Wenting Zheng, Stephen Tu, Eddie Kohler, Barbara Liskov:
Fast Databases with Fast Durability and Recovery Through Multicore Parallelism. OSDI 2014: 465-477 - [i1]Raphael Bost, Raluca Ada Popa, Stephen Tu, Shafi Goldwasser:
Machine Learning Classification over Encrypted Data. IACR Cryptol. ePrint Arch. 2014: 331 (2014) - 2013
- [j2]Stephen Tu, M. Frans Kaashoek, Samuel Madden, Nickolai Zeldovich:
Processing Analytical Queries over Encrypted Data. Proc. VLDB Endow. 6(5): 289-300 (2013) - [j1]Justin A. DeBrabant, Andrew Pavlo
Anti-Caching: A New Approach to Database Management System Architecture. Proc. VLDB Endow. 6(14): 1942-1953 (2013) - [c4]Stephen Tu, Wenting Zheng, Eddie Kohler, Barbara Liskov, Samuel Madden:
Speedy transactions in multicore in-memory databases. SOSP 2013: 18-32 - 2012
- [c3]Haiping Zhao, Iain Proctor, Minghui Yang, Xin Qi, Mark Williams, Qi Gao, Guilherme Ottoni, Andrew Paroski, Scott MacVicar, Jason Evans, Stephen Tu:
The HipHop compiler for PHP. OOPSLA 2012: 575-586 - 2010
- [c2]Michael Armbrust, Nick Lanham, Stephen Tu, Armando Fox, Michael J. Franklin, David A. Patterson:
The case for PIQL: a performance insightful query language. SoCC 2010: 131-136 - [c1]Michael Armbrust, Stephen Tu, Armando Fox, Michael J. Franklin, David A. Patterson, Nick Lanham, Beth Trushkowsky, Jesse Trutna:
PIQL: a performance insightful query language. SIGMOD Conference 2010: 1207-1210
Coauthor Index
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