44 citations · 142 across the 15 of their papers we have counts for
10 papers · 1 filter
Damped Online Newton Step for Portfolio Selection
Zakaria Mhammedi, Alexander Rakhlin
We revisit the classic online portfolio selection problem, where at each round a learner selects a distribution over a set of portfolios to allocate its wealth. It is known that fo…
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz +5
We introduce a new problem setting for continuous control called the LQR with Rich Observations, or RichLQR. In our setting, the environment is summarized by a low-dimensional cont…
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi +1
In the classical multi-armed bandit problem, instance-dependent algorithms attain improved performance on "easy" problems with a gap between the best and second-best arm. Are simil…
Learning nonlinear dynamical systems from a single trajectory
Dylan J. Foster, Alexander Rakhlin, Tuhin Sarkar
We introduce algorithms for learning nonlinear dynamical systems of the form , where is a weight matrix, is a nonlinear link…
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
Dylan J. Foster, Alexander Rakhlin
A fundamental challenge in contextual bandits is to develop flexible, general-purpose algorithms with computational requirements no worse than classical supervised learning tasks s…
Vector Contraction for Rademacher Complexity
Dylan J. Foster, Alexander Rakhlin
We show that the Rademacher complexity of any -valued function class composed with an -Lipschitz function is bounded by the maximum Rademacher comple…