123 citations · 534 across the 48 of their papers we have counts for
67 papers
Data Deletion Can Help in Adaptive RL
Param Budhraja, Aditya Gangrade, Alex Olshevsky +1
Deploying reinforcement learning policies in the real world requires adapting to time-varying environments. We study this problem in the contextual Markov Decision Process (cMDP) f…
Interpretable Gradient Descent for Kalman Gain
M. A. Belabbas, A. Olshevsky
We derive a decomposition for the gradient of the innovation loss with respect to the filter gain in a linear time-invariant system, decomposing as a product of an observability Gr…
Sample Complexity of Linear Quadratic Regulator Without Initial Stability
Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard
Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown dynamics. Unlike prior methods, our algorithm a…
Tree Search for Simultaneous Move Games via Equilibrium Approximation
Ryan Yu, Alex Olshevsky, Peter Chin
Neural network supported tree-search has shown strong results in a variety of perfect information multi-agent tasks. However, the performance of these methods on partial informatio…
Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens
Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard
We provide the first known algorithm that provably achieves -optimality within function evaluations for the discounted discret…
Convex SGD: Generalization Without Early Stopping
Julien Hendrickx, Alex Olshevsky
We consider the generalization error associated with stochastic gradient descent on a smooth convex function over a compact set. We show the first bound on the generalization error…