Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks. - Anton Dereventsov; Armenak Petrosyan; Clayton G. Webster (CoRR, 2019)

Neural network integral representations with the ReLU activation function. - Armenak Petrosyan; Anton Dereventsov; Clayton G. Webster (MSML, 2020)

Biorthogonal greedy algorithms in convex optimization. - Anton Dereventsov; Vladimir N. Temlyakov (CoRR, 2020)

An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization. - Anton Dereventsov; Clayton G. Webster; Joseph D. Daws Jr. (CoRR, 2020)

On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks. - Anton Dereventsov; Ranga Raju Vatsavai; Clayton G. Webster (ICDM (Workshops), 2021)

Offline Policy Comparison under Limited Historical Agent-Environment Interactions. - Anton Dereventsov; Joseph D. Daws Jr.; Clayton G. Webster (CoRR, 2021)

Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets. - Anton Dereventsov; Anton Bibin (ICDM (Workshops), 2022)

Modeling Non-deterministic Human Behaviors in Discrete Food Choices. - Andrew Starnes; Anton Dereventsov; E. Susanne Blazek; Folasade Phillips (ICDM (Workshops), 2022)

Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging. - Rachel M. Harrison; Anton Dereventsov; Anton Bibin (ICDM (Workshops), 2023)

Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks. - Andrew Starnes; Anton Dereventsov; Clayton G. Webster (ICDM (Workshops), 2023)

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks. - Anton Dereventsov; Andrew Starnes; Clayton G. Webster (ICPRAI (1), 2024)

An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner's Guide. - Oluwole Fagbohun; Rachel M. Harrison; Anton Dereventsov (CoRR, 2024)

Data-Centric Approach to Constrained Machine Learning: A Case Study on Conway's Game of Life. - Anton Bibin; Anton Dereventsov (CoRR, 2024)