Tu, Stephen

36 publications

NeurIPS 2025 Integration Matters for Learning PDEs with Backward SDEs Sungje Park, Stephen Tu

ICLR 2025 Shallow Diffusion Networks Provably Learn Hidden Low-Dimensional Structure Nicholas Matthew Boffi, Arthur Jacot, Stephen Tu, Ingvar Ziemann

ICMLW 2024 In-Context Generalization to New Tasks from Unlabeled Observation Data Anthony Liang, Pavel Czempin, Yutai Zhou, Stephen Tu, Erdem Biyik

JMLR 2024 Learning from Many Trajectories Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi

TMLR 2024 Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models Sumeet Singh, Stephen Tu, Vikas Sindhwani

ICML 2024 Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

L4DC 2023 Agile Catching with Whole-Body MPC and Blackbox Policy Learning 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 Slotine, Stephen Tu

ICML 2023 Bootstrapped Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney

ICLRW 2023 Bootstrapped Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney

L4DC 2023 Multi-Task Imitation Learning for Linear Dynamical Systems Thomas T. Zhang, Katie Kang, Bruce D Lee, Claire Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni

CoRL 2023 Robots That Ask for Help: Uncertainty Alignment for Large Language Model Planners 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

NeurIPS 2023 The Noise Level in Linear Regression with Dependent Data Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

ICML 2023 The Power of Learned Locally Linear Models for Nonlinear Policy Optimization Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu

AISTATS 2022 On the Generalization of Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare

AISTATS 2022 The Role of Optimization Geometry in Single Neuron Learning Nicholas Boffi, Stephen Tu, Jean-Jacques Slotine

L4DC 2022 Adversarially Robust Stability Certificates Can Be Sample-Efficient Thomas Zhang, Stephen Tu, Nicholas Boffi, Jean-Jacques Slotine, Nikolai Matni

CoRL 2022 Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation Xuesu Xiao, Tingnan Zhang, Krzysztof Marcin Choromanski, Tsang-Wei Edward Lee, Anthony 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

NeurIPS 2022 Learning with Little Mixing Ingvar Ziemann, Stephen Tu

JMLR 2022 Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

L4DC 2022 On the Sample Complexity of Stability Constrained Imitation Learning Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni

NeurIPS 2022 TaSIL: Taylor Series Imitation Learning Daniel Pfrommer, Thomas Zhang, Stephen Tu, Nikolai Matni

NeurIPSW 2022 Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based RL David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley

L4DC 2021 Regret Bounds for Adaptive Nonlinear Control Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

L4DC 2021 Safely Learning Dynamical Systems from Short Trajectories Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

CoRL 2020 Learning Hybrid Control Barrier Functions from Data Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos Dimarogonas, Stephen Tu, Nikolai Matni

CoRL 2020 Learning Stability Certificates from Data Nicholas Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques Slotine, Vikas Sindhwani

ICLR 2020 Observational Overfitting in Reinforcement Learning Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur

NeurIPS 2019 Certainty Equivalence Is Efficient for Linear Quadratic Control Horia Mania, Stephen Tu, Benjamin Recht

NeurIPS 2019 Finite-Time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator Karl Krauth, Stephen Tu, Benjamin Recht

COLT 2019 The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint Stephen Tu, Benjamin Recht

COLT 2018 Learning Without Mixing: Towards a Sharp Analysis of Linear System Identification Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht

ICML 2018 Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator Stephen Tu, Benjamin Recht

NeurIPS 2018 Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu

ICML 2017 Breaking Locality Accelerates Block Gauss-Seidel Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht

NeurIPS 2016 Cyclades: Conflict-Free Asynchronous Machine Learning Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I Jordan, Kannan Ramchandran, Christopher Ré

ICML 2016 Low-Rank Solutions of Linear Matrix Equations via Procrustes Flow Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht