120 citations · 245 across the 34 of their papers we have counts for
36 papers
Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
Anton Dereventsov, Andrew Starnes, Clayton G. Webster
This effort is focused on examining the behavior of reinforcement learning systems in personalization environments and detailing the differences in policy entropy associated with t…
On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks
Anton Dereventsov, Ranga Raju Vatsavai, Clayton Webster
In this effort we consider a reinforcement learning (RL) technique for solving personalization tasks with complex reward signals. In particular, our approach is based on state spac…
An asymptotically compatible probabilistic collocation method for randomly heterogeneous nonlocal problems
Yiming Fan, Xiaochuan Tian, Xiu Yang +3
In this paper we present an asymptotically compatible meshfree method for solving nonlocal equations with random coefficients, describing diffusion in heterogeneous media. In parti…
Offline Policy Comparison under Limited Historical Agent-Environment Interactions
Anton Dereventsov, Joseph D. Daws, Clayton Webster
We address the challenge of policy evaluation in real-world applications of reinforcement learning systems where the available historical data is limited due to ethical, practical,…
An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization
Anton Dereventsov, Clayton G. Webster, Joseph D. Daws
In this work, we propose a novel adaptive stochastic gradient-free (ASGF) approach for solving high-dimensional nonconvex optimization problems based on function evaluations. We em…
Analysis of The Ratio of and Norms in Compressed Sensing
Yiming Xu, Akil Narayan, Hoang Tran +1
We first propose a novel criterion that guarantees that an -sparse signal is the local minimizer of the objective; our criterion is interpretable and useful in p…