4 citations · 7 across the 5 of their papers we have counts for
5 papers
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 Deep Neural Networks with Quasi-optimal polynomial approximation rates
Joseph Daws, Clayton Webster
We show the existence of a deep neural network capable of approximating a wide class of high-dimensional approximations. The construction of the proposed neural network is based on…
A Weighted -Minimization Approach For Wavelet Reconstruction of Signals and Images
Joseph Daws, Armenak Petrosyan, Hoang Tran +1
In this effort, we propose a convex optimization approach based on weighted -regularization for reconstructing objects of interest, such as signals or images, that are spar…
A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks
Joseph Daws, Clayton G. Webster
In this effort we propose a novel approach for reconstructing multivariate functions from training data, by identifying both a suitable network architecture and an initialization u…