Stephen Tu

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 81

Top Topics

Dynamical Systems

Temporal Difference Learning

Reinforcement Learning

Linear Quadratic

Top Venues

Publications

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 (2024)
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 (2023)
Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
The noise level in linear regression with dependent data. CoRR (2023)
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. CoRL (2023)
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)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. CoRR (2023)
Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. CoRR (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. ICML (2023)
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)
Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
The noise level in linear regression with dependent data. NeurIPS (2023)
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 (2023)
Sumeet Singh, Stephen Tu, Vikas Sindhwani
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models. CoRR (2023)
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)
Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
Safely Learning Dynamical Systems. CoRR (2023)
Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. ICML (2023)
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 (2022)
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 (2022)
Ingvar M. Ziemann, Stephen Tu
Learning with little mixing. CoRR (2022)
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
Nonparametric adaptive control and prediction: theory and randomized algorithms. J. Mach. Learn. Res. 23 (2022)
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 (2022)
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)
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)
Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
On the Generalization of Representations in Reinforcement Learning. AISTATS (2022)
Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni
On the Sample Complexity of Stability Constrained Imitation Learning. L4DC (2022)
Ingvar M. Ziemann, Stephen Tu
Learning with little mixing. NeurIPS (2022)
Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
Learning from many trajectories. CoRR (2022)
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
The role of optimization geometry in single neuron learning. AISTATS (2022)
Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni
TaSIL: Taylor Series Imitation Learning. CoRR (2022)
Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
On the Generalization of Representations in Reinforcement Learning. CoRR (2022)
Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni
TaSIL: Taylor Series Imitation Learning. NeurIPS (2022)
Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. ADHS (2021)
Stephen Tu, Alexander Robey, Nikolai Matni
Closing the Closed-Loop Distribution Shift in Safe Imitation Learning. CoRR (2021)
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms. CDC (2021)
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 (2021)
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
Random features for adaptive nonlinear control and prediction. CoRR (2021)
Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. CoRR (2021)
Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
Safely Learning Dynamical Systems from Short Trajectories. L4DC (2021)
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
Regret Bounds for Adaptive Nonlinear Control. L4DC (2021)
Lars Lindemann, Alexander Robey, Lejun Jiang, Stephen Tu, Nikolai Matni
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations. CoRR (2021)
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
On the Sample Complexity of the Linear Quadratic Regulator. Found. Comput. Math. 20 (4) (2020)
Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
Safely Learning Dynamical Systems from Short Trajectories. CoRR (2020)
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
Regret Bounds for Adaptive Nonlinear Control. CoRR (2020)
Octavio Narvaez-Aroche, Pierre-Jean Meyer, Stephen Tu, Andrew K. Packard, Murat Arcak
Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis. IEEE Trans. Control. Syst. Technol. 28 (6) (2020)
Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani
Learning Stability Certificates from Data. CoRL (2020)
Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
Learning Control Barrier Functions from Expert Demonstrations. CoRR (2020)
Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
Learning Control Barrier Functions from Expert Demonstrations. CDC (2020)
Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
Learning Hybrid Control Barrier Functions from Data. CoRR (2020)
Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
Learning Hybrid Control Barrier Functions from Data. CoRL (2020)
Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani
Learning Stability Certificates from Data. CoRR (2020)
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur
Observational Overfitting in Reinforcement Learning. ICLR (2020)
Nikolai Matni, Stephen Tu
A Tutorial on Concentration Bounds for System Identification. CDC (2019)
Nikolai Matni, Stephen Tu
A Tutorial on Concentration Bounds for System Identification. CoRR (2019)
Karl Krauth, Stephen Tu, Benjamin Recht
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. CoRR (2019)
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur
Observational Overfitting in Reinforcement Learning. CoRR (2019)
Horia Mania, Stephen Tu, Benjamin Recht
Certainty Equivalent Control of LQR is Efficient. CoRR (2019)
Nikolai Matni, Alexandre Proutière, Anders Rantzer, Stephen Tu
From self-tuning regulators to reinforcement learning and back again. CDC (2019)
Horia Mania, Stephen Tu, Benjamin Recht
Certainty Equivalence is Efficient for Linear Quadratic Control. NeurIPS (2019)
Nikolai Matni, Alexandre Proutière, Anders Rantzer, Stephen Tu
From self-tuning regulators to reinforcement learning and back again. CoRR (2019)
Stephen Tu, Benjamin Recht
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint. COLT (2019)
Stephen Tu, Ross Boczar, Benjamin Recht
Minimax Lower Bounds for H∞-Norm Estimation. ACC (2019)
Karl Krauth, Stephen Tu, Benjamin Recht
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. NeurIPS (2019)
Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht
Safely Learning to Control the Constrained Linear Quadratic Regulator. ACC (2019)
Stephen Tu, Ross Boczar, Benjamin Recht
-Norm Estimation. CoRR (2018)
Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. COLT (2018)
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. CoRR (2018)
Stephen Tu, Ross Boczar, Benjamin Recht
-norm Estimation. ACC (2018)
Vikas Sindhwani, Stephen Tu, Mohi Khansari
Learning Contracting Vector Fields For Stable Imitation Learning. CoRR (2018)
Octavio Narvaez-Aroche, Pierre-Jean Meyer, Stephen Tu, Andrew K. Packard, Murat Arcak
Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis. CoRR (2018)
Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. CoRR (2018)
Stephen Tu, Benjamin Recht
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. ICML (2018)
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. NeurIPS (2018)
Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht
Safely Learning to Control the Constrained Linear Quadratic Regulator. CoRR (2018)
Stephen Tu, Benjamin Recht
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint. CoRR (2018)
Stephen Tu, Ross Boczar, Benjamin Recht
On the Approximation of Toeplitz Operators for Nonparametric $\mathcal{H}_\infty$-norm Estimation. CoRR (2017)
Stephen Tu, Benjamin Recht
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. CoRR (2017)
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
On the Sample Complexity of the Linear Quadratic Regulator. CoRR (2017)
Stephen Tu, Ross Boczar, Andrew K. Packard, Benjamin Recht
Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification. CoRR (2017)
Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht
Breaking Locality Accelerates Block Gauss-Seidel. ICML (2017)
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht
Large Scale Kernel Learning using Block Coordinate Descent. CoRR (2016)
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 (2016)
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. ICML (2016)
Raphael Bost, Raluca Ada Popa, Stephen Tu, Shafi Goldwasser
Machine Learning Classification over Encrypted Data. NDSS (2015)
Raphael Bost, Raluca Ada Popa, Stephen Tu, Shafi Goldwasser
Machine Learning Classification over Encrypted Data. IACR Cryptol. ePrint Arch. 2014 (2014)
Wenting Zheng, Stephen Tu, Eddie Kohler, Barbara Liskov
Fast Databases with Fast Durability and Recovery Through Multicore Parallelism. OSDI (2014)
Stephen Tu, Wenting Zheng, Eddie Kohler, Barbara Liskov, Samuel Madden
Speedy transactions in multicore in-memory databases. SOSP (2013)
Stephen Tu, M. Frans Kaashoek, Samuel Madden, Nickolai Zeldovich
Processing Analytical Queries over Encrypted Data. Proc. VLDB Endow. 6 (5) (2013)
Justin A. DeBrabant, Andrew Pavlo, Stephen Tu, Michael Stonebraker, Stanley B. Zdonik
Anti-Caching: A New Approach to Database Management System Architecture. Proc. VLDB Endow. 6 (14) (2013)
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)
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)
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)