The case for PIQL: a performance insightful query language. - Michael Armbrust; Nick Lanham; Stephen Tu; Armando Fox; Michael J. Franklin; David A. Patterson 001 (SoCC, 2010)

PIQL: a performance insightful query language. - Michael Armbrust; Stephen Tu; Armando Fox; Michael J. Franklin; David A. Patterson 001; Nick Lanham; Beth Trushkowsky; Jesse Trutna (SIGMOD Conference, 2010)

The HipHop compiler for PHP. - Haiping Zhao; Iain Proctor; Minghui Yang; Xin Qi; Mark Williams; Qi Gao; Guilherme Ottoni; Andrew Paroski; Scott MacVicar; Jason Evans; Stephen Tu (OOPSLA, 2012)

Speedy transactions in multicore in-memory databases. - Stephen Tu; Wenting Zheng; Eddie Kohler; Barbara Liskov; Samuel Madden 001 (SOSP, 2013)

Anti-Caching: A New Approach to Database Management System Architecture. - Justin A. DeBrabant; Andrew Pavlo; Stephen Tu; Michael Stonebraker; Stanley B. Zdonik (Proc. VLDB Endow., 2013)

Processing Analytical Queries over Encrypted Data. - Stephen Tu; M. Frans Kaashoek; Samuel Madden 001; Nickolai Zeldovich (Proc. VLDB Endow., 2013)

Fast Databases with Fast Durability and Recovery Through Multicore Parallelism. - Wenting Zheng; Stephen Tu; Eddie Kohler; Barbara Liskov (OSDI, 2014)

Machine Learning Classification over Encrypted Data. - Raphael Bost; Raluca Ada Popa; Stephen Tu; Shafi Goldwasser (IACR Cryptol. ePrint Arch., 2014)

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. - Stephen Tu; Ross Boczar; Max Simchowitz; Mahdi Soltanolkotabi; Ben Recht (ICML, 2016)

CYCLADES: Conflict-free Asynchronous Machine Learning. - Xinghao Pan; Maximilian Lam; Stephen Tu; Dimitris S. Papailiopoulos; Ce Zhang 001; Michael I. Jordan; Kannan Ramchandran; Christopher Ré; Benjamin Recht (CoRR, 2016)

Large Scale Kernel Learning using Block Coordinate Descent. - Stephen Tu; Rebecca Roelofs; Shivaram Venkataraman; Benjamin Recht (CoRR, 2016)

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

Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification. - Stephen Tu; Ross Boczar; Andrew K. Packard; Benjamin Recht (CoRR, 2017)

On the Approximation of Toeplitz Operators for Nonparametric $ mathcal{H}_ infty$-norm Estimation. - Stephen Tu; Ross Boczar; Benjamin Recht (CoRR, 2017)

On the Approximation of Toeplitz Operators for Nonparametric H∞-norm Estimation. - Stephen Tu; Ross Boczar; Benjamin Recht (ACC, 2018)

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

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

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

Learning Contracting Vector Fields For Stable Imitation Learning. - Vikas Sindhwani; Stephen Tu; Mohi Khansari (CoRR, 2018)

Minimax Lower Bounds for ℋ∞-Norm Estimation. - Stephen Tu; Ross Boczar; Benjamin Recht (CoRR, 2018)

Safely Learning to Control the Constrained Linear Quadratic Regulator. - Sarah Dean; Stephen Tu; Nikolai Matni; Benjamin Recht (ACC, 2019)

Minimax Lower Bounds for H∞-Norm Estimation. - Stephen Tu; Ross Boczar; Benjamin Recht (ACC, 2019)

From self-tuning regulators to reinforcement learning and back again. - Nikolai Matni; Alexandre Proutière; Anders Rantzer; Stephen Tu (CDC, 2019)

A Tutorial on Concentration Bounds for System Identification. - Nikolai Matni; Stephen Tu (CDC, 2019)

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

Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. - Karl Krauth; Stephen Tu; Benjamin Recht (NeurIPS, 2019)

Certainty Equivalence is Efficient for Linear Quadratic Control. - Horia Mania; Stephen Tu; Benjamin Recht (NeurIPS, 2019)

Certainty Equivalent Control of LQR is Efficient. - Horia Mania; Stephen Tu; Benjamin Recht (CoRR, 2019)

Sample Complexity Bounds for the Linear Quadratic Regulator. - Stephen Tu (2019)

Learning Control Barrier Functions from Expert Demonstrations. - Alexander Robey; Haimin Hu; Lars Lindemann; Hanwen Zhang; Dimos V. Dimarogonas; Stephen Tu; Nikolai Matni (CDC, 2020)

Learning Stability Certificates from Data. - Nicholas M. Boffi; Stephen Tu; Nikolai Matni; Jean-Jacques E. Slotine; Vikas Sindhwani (CoRL, 2020)

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

Observational Overfitting in Reinforcement Learning. - Xingyou Song; Yiding Jiang; Stephen Tu; Yilun Du; Behnam Neyshabur (ICLR, 2020)

On the Sample Complexity of the Linear Quadratic Regulator. - Sarah Dean; Horia Mania; Nikolai Matni; Benjamin Recht; Stephen Tu (Found. Comput. Math., 2020)

Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis. - Octavio Narvaez-Aroche; Pierre-Jean Meyer; Stephen Tu; Andrew K. Packard; Murat Arcak (IEEE Trans. Control. Syst. Technol., 2020)

Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. - Alexander Robey; Lars Lindemann; Stephen Tu; Nikolai Matni (ADHS, 2021)

Safely Learning Dynamical Systems from Short Trajectories. - Amir Ali Ahmadi; Abraar Chaudhry; Vikas Sindhwani; Stephen Tu (L4DC, 2021)

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

Closing the Closed-Loop Distribution Shift in Safe Imitation Learning. - Stephen Tu; Alexander Robey; Nikolai Matni (CoRR, 2021)

Random features for adaptive nonlinear control and prediction. - Nicholas M. Boffi; Stephen Tu; Jean-Jacques E. Slotine (CoRR, 2021)

Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations. - Lars Lindemann; Alexander Robey; Lejun Jiang; Stephen Tu; Nikolai Matni (CoRR, 2021)

The role of optimization geometry in single neuron learning. - Nicholas M. Boffi; Stephen Tu; Jean-Jacques E. Slotine (AISTATS, 2022)

On the Generalization of Representations in Reinforcement Learning. - Charline Le Lan; Stephen Tu; Adam Oberman; Rishabh Agarwal; Marc G. Bellemare (AISTATS, 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 G. Francis; Jake Varley; Stephen Tu; Sumeet Singh; Peng Xu 010; Fei Xia 002; Sven Mikael Persson; Dmitry Kalashnikov; et al. (CoRL, 2022)

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

Adversarially Robust Stability Certificates can be Sample-Efficient. - Thomas T. C. K. Zhang; Stephen Tu; Nicholas M. Boffi; Jean-Jacques E. Slotine; Nikolai Matni (L4DC, 2022)

TaSIL: Taylor Series Imitation Learning. - Daniel Pfrommer; Thomas T. C. K. Zhang; Stephen Tu; Nikolai Matni (NeurIPS, 2022)

Learning with little mixing. - Ingvar M. Ziemann; Stephen Tu (NeurIPS, 2022)

Nonparametric adaptive control and prediction: theory and randomized algorithms. - Nicholas M. Boffi; Stephen Tu; Jean-Jacques E. Slotine (J. Mach. Learn. Res., 2022)

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 010; Leila Takayama; Fei Xia 002; Jake Varley; Zhenjia Xu; Dorsa Sadigh; et al. (CoRL, 2023)

Bootstrapped Representations in Reinforcement Learning. - Charline Le Lan; Stephen Tu; Mark Rowland 001; Anna Harutyunyan; Rishabh Agarwal; Marc G. Bellemare; Will Dabney (ICML, 2023)

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

Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. - David Brandfonbrener; Stephen Tu; Avi Singh; Stefan Welker; Chad Boodoo; Nikolai Matni; Jake Varley (ICRA, 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 E. Slotine; Stephen Tu (L4DC, 2023)

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

The noise level in linear regression with dependent data. - Ingvar M. Ziemann; Stephen Tu; George J. Pappas; Nikolai Matni (NeurIPS, 2023)

Safely Learning Dynamical Systems. - Amir Ali Ahmadi; Abraar Chaudhry; Vikas Sindhwani; Stephen Tu (CoRR, 2023)

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

Incremental Composition of Learned Control Barrier Functions in Unknown Environments. - Paul Lutkus; Deepika Anantharaman; Stephen Tu; Lars Lindemann (CoRR, 2024)

Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem. - Hesameddin Mohammadi; Mohammad Tinati; Stephen Tu; Mahdi Soltanolkotabi; Mihailo R. Jovanovic (CoRR, 2024)

Learning from many trajectories. - Stephen Tu; Roy Frostig; Mahdi Soltanolkotabi (J. Mach. Learn. Res., 2024)

Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models. - Sumeet Singh; Stephen Tu; Vikas Sindhwani (Trans. Mach. Learn. Res., 2024)

Shallow diffusion networks provably learn hidden low-dimensional structure. - Nicholas Matthew Boffi; Arthur Jacot; Stephen Tu; Ingvar M. Ziemann (ICLR, 2025)

Data-Driven Reachability with Scenario Optimization and the Holdout Method. - Elizabeth Dietrich; Rosalyn A. Devonport; Stephen Tu; Murat Arcak (CoRR, 2025)

Integration Matters for Learning PDEs with Backwards SDEs. - Sungje Park; Stephen Tu (CoRR, 2025)

CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations. - Anthony Liang; Pavel Czempin; Matthew Hong 002; Yutai Zhou; Erdem Biyik; Stephen Tu (CoRR, 2025)

Latent Representations for Control Design with Provable Stability and Safety Guarantees. - Paul Lutkus; Kaiyuan Wang; Lars Lindemann; Stephen Tu (CoRR, 2025)