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Stephen Tu

Random features for adaptive nonlinear control and prediction


Jun 07, 2021
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine


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Closing the Closed-Loop Distribution Shift in Safe Imitation Learning


Feb 18, 2021
Stephen Tu, Alexander Robey, Nikolai Matni

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Learning Robust Hybrid Control Barrier Functions for Uncertain Systems


Jan 16, 2021
Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni

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* 17 pages, 7th IFAC Conference on Analysis and Design of Hybrid Systems (submitted). arXiv admin note: text overlap with arXiv:2011.04112 
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Regret Bounds for Adaptive Nonlinear Control


Nov 26, 2020
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

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Safely Learning Dynamical Systems from Short Trajectories


Nov 24, 2020
Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

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Learning Hybrid Control Barrier Functions from Data


Nov 08, 2020
Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni

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* 27 pages, Conference on Robot Learning 2020 
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Learning Stability Certificates from Data


Sep 14, 2020
Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani

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* Fixes an error in the statement and proof of Theorem 5.1, Theorem 5.2, and Proposition D.1 
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Learning Control Barrier Functions from Expert Demonstrations


Apr 07, 2020
Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni

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Observational Overfitting in Reinforcement Learning


Dec 28, 2019
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur

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* Published as a conference paper in ICLR 2020 
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A Tutorial on Concentration Bounds for System Identification


Jun 27, 2019
Nikolai Matni, Stephen Tu

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* Tutorial paper submitted to 2020 IEEE Conference on Decision and Control 
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From self-tuning regulators to reinforcement learning and back again


Jun 27, 2019
Nikolai Matni, Alexandre Proutiere, Anders Rantzer, Stephen Tu

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* Tutorial paper submitted to 2020 IEEE Conference on Decision and Control 
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Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator


May 30, 2019
Karl Krauth, Stephen Tu, Benjamin Recht

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Certainty Equivalent Control of LQR is Efficient


Feb 21, 2019
Horia Mania, Stephen Tu, Benjamin Recht

* 21 pages 
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The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint


Dec 09, 2018
Stephen Tu, Benjamin Recht


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Minimax Lower Bounds for $\mathcal{H}_\infty$-Norm Estimation


Sep 28, 2018
Stephen Tu, Ross Boczar, Benjamin Recht

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Safely Learning to Control the Constrained Linear Quadratic Regulator


Sep 26, 2018
Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht

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Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification


May 24, 2018
Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht


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Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator


May 23, 2018
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu

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Learning Contracting Vector Fields For Stable Imitation Learning


Apr 13, 2018
Vikas Sindhwani, Stephen Tu, Mohi Khansari

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On the Sample Complexity of the Linear Quadratic Regulator


Jan 25, 2018
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu

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* Contains a new convex relaxation for the main optimization problem that is computationally inexpensive and performs well in simulation. The new relaxation uses a common Lyapunov function formulation for the mixed H2/H-infinity synthesis problem. We added new section on computational methods and new numerical experiments incorporating this technique. We also corrected typos and added references 
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Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator


Dec 22, 2017
Stephen Tu, Benjamin Recht

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Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification


Nov 30, 2017
Stephen Tu, Ross Boczar, Andrew Packard, Benjamin Recht

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* A substantial revision, where we strengthen our existing upper bounds and introduce a matching lower bound 
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CYCLADES: Conflict-free Asynchronous Machine Learning


May 31, 2016
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Chris Re, Benjamin Recht

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Large Scale Kernel Learning using Block Coordinate Descent


Feb 17, 2016
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht

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