Stephen Tu

I am a graduate student in EECS at UC Berkeley advised by Ben Recht. My research interests lie in the intersection of machine learning, optimization, and control theory. I am generously supported by a Google PhD Fellowship in Machine Learning.

The easiest way to reach me is e-mail: stephent at berkeley dot edu


Certainty Equivalent Control of LQR is Efficient. [arXiv]
Horia Mania, Stephen Tu, and Benjamin Recht.

Learning Contracting Vector Fields For Stable Imitation Learning. [arXiv]
Vikas Sindhwani, Stephen Tu, and Mohi Khansari.

Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification. [arXiv]
Stephen Tu, Ross Boczar, Andrew Packard, and Benjamin Recht.

Large Scale Kernel Learning using Block Coordinate Descent. [arXiv]
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, and Benjamin Recht.


The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint. [PDF]
Stephen Tu and Benjamin Recht.
To appear in COLT 2019.

On the Sample Complexity of the Linear Quadratic Regulator. [PDF]
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu.
To appear in Foundations of Computational Mathematics.

Minimax Lower Bounds for H-Infinity-Norm Estimation. [PDF]
Stephen Tu*, Ross Boczar*, and Benjamin Recht.
* Equal contribution.
To appear in ACC 2019.

Safely Learning to Control the Constrained Linear Quadratic Regulator. [PDF]
Sarah Dean, Stephen Tu, Nikolai Matni, and Benjamin Recht.
To appear in ACC 2019.

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

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. [PDF]
Stephen Tu and Benjamin Recht.
ICML 2018.

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

On the Approximation of Toeplitz Operators for Nonparametric H-infinity-norm Estimation. [PDF]
Stephen Tu, Ross Boczar, and Benjamin Recht.
ACC 2018.

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

Cyclades: Conflict-free Asynchronous Machine Learning. [PDF]
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, and Benjamin Recht.
NeurIPS 2016.

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. [PDF] [Slides]
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, and Benjamin Recht.
ICML 2016.

Machine Learning Classification over Encrypted Data. [PDF]
Raphael Bost, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser.
NDSS 2015.

Fast Databases with Fast Durability and Recovery through Multicore Parallelism. [PDF]
Wenting Zheng, Stephen Tu, Eddie Kohler, and Barbara Liskov.
OSDI 2014.

Anti-Caching: A New Approach to Swapping in Main Memory OLTP Database Systems. [PDF]
Justin DeBrabant, Andrew Pavlo, Stephen Tu, Michael Stonebraker, and Stan Zdonik.
VLDB 2014.

Speedy Transactions in Multicore In-Memory Databases. [PDF] [Slides] [Code]
Stephen Tu, Wenting Zheng, Eddie Kohler, Barbara Liskov, and Samuel Madden.
SOSP 2013.

Processing Analytical Queries over Encrypted Data. [PDF] [Slides] [Code]
Stephen Tu, M. Frans Kaashoek, Samuel Madden, and Nickolai Zeldovich.
VLDB 2013.

The HipHop Compiler for PHP.
Haiping Zhao, Iain Proctor, Minghui Yang, Xin Qi, Mark Williams, Guilherme Ottoni, Charlie Gao, Andrew Paroski, Scott MacVicar, Jason Evans, and Stephen Tu.
OOPSLA 2012.

The Case for PIQL: A Performance Insightful Query Language. [PDF]
Michael Armbrust, Nick Lanham, Stephen Tu, Armando Fox, Michael Franklin, and David Patterson.
SoCC 2010.

PIQL: A Performance Insightful Query Language For Interactive Applications. [PDF]
Michael Armbrust, Stephen Tu, Armando Fox, Michael Franklin, David Patterson, Nick Lanham, Beth Trushkowsky, and Jesse Trutna.
SIGMOD 2010, Demonstration.


Sample Complexity Bounds for the Linear Quadratic Regulator. [PDF]
PhD Thesis, University of California, Berkeley. Spring 2019.


Learning mixture models. [PDF]

Practical first order methods for large scale semidefinite programming. [PDF]

Geometric random walks for sampling from convex bodies. [PDF]

data-microscopes: Bayesian non-parametric inference made simple in Python. [Slides]

The Dirichlet-Multinomial and Dirichlet-Categorical models for Bayesian inference. [PDF]

Derivation of EM updates for discrete Hidden Markov Models. [PDF]

Introductory notes on differential privacy. [PDF]

Techniques for query processing on encrypted databases. [PDF]

Implementing concurrent data structures on modern multicore machines. [Slides] [Examples]