54 citations · 81 across the 2 of their papers we have counts for
4 papers
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Stephen Tu, Benjamin Recht
Reinforcement learning (RL) has been successfully used to solve many continuous control tasks. Despite its impressive results however, fundamental questions regarding the sample co…
Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification
Stephen Tu, Ross Boczar, Andrew Packard +1
This work explores the trade-off between the number of samples required to accurately build models of dynamical systems and the degradation of performance in various control object…
CYCLADES: Conflict-free Asynchronous Machine Learning
Xinghao Pan, Maximilian Lam, Stephen Tu +6
We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during shared model updates, and…
Large Scale Kernel Learning using Block Coordinate Descent
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman +1
We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points. Armed with this capability, w…