23 citations · 47 across the 17 of their papers we have counts for
17 papers
Data-Centric Approach to Constrained Machine Learning: A Case Study on Conway's Game of Life
Anton Bibin, Anton Dereventsov
This paper focuses on a data-centric approach to machine learning applications in the context of Conway's Game of Life. Specifically, we consider the task of training a minimal arc…
Gaussian smoothing gradient descent for minimizing functions (GSmoothGD)
Andrew Starnes, Anton Dereventsov, Clayton Webster
This work analyzes the convergence of a class of smoothing-based gradient descent methods when applied to optimization problems. In particular, Gaussian smoothing is employed to de…
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks
Andrew Starnes, Anton Dereventsov, Clayton Webster
In this effort, we consider the impact of regularization on the diversity of actions taken by policies generated from reinforcement learning agents trained using a policy gradient.…
Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging
Rachel M. Harrison, Anton Dereventsov, Anton Bibin
We present a method for zero-shot recommendation of multimodal non-stationary content that leverages recent advancements in the field of generative AI. We propose rendering inputs…
Modeling Non-deterministic Human Behaviors in Discrete Food Choices
Andrew Starnes, Anton Dereventsov, E. Susanne Blazek +1
We establish a non-deterministic model that predicts a user's food preferences from their demographic information. Our simulator is based on NHANES dataset and domain expert knowle…
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…
Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets
Anton Dereventsov, Anton Bibin
We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, Last.fm, Million Song, e…
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…
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…
Biorthogonal Greedy Algorithms in Convex Optimization
Anton Dereventsov, Vladimir Temlyakov
The study of greedy approximation in the context of convex optimization is becoming a promising research direction as greedy algorithms are actively being employed to construct spa…
Neural network integral representations with the ReLU activation function
Armenak Petrosyan, Anton Dereventsov, Clayton Webster
In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function. We assume that the outer weighs admit a finite $L…
Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks
Anton Dereventsov, Armenak Petrosyan, Clayton Webster
We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function. In our approach we obt…
The Natural Greedy Algorithm for reduced bases in Banach spaces
Anton Dereventsov, Clayton Webster
In this effort we introduce and analyze a novel reduced basis approach, used to construct an approximating subspace for a given set of data. Our technique, which we call the Natura…
A unified way of analyzing some greedy algorithms
Anton Dereventsov, Vladimir Temlyakov
In this paper we propose a unified way of analyzing a certain kind of greedy-type algorithms in Banach spaces. We define a class of the Weak Biorthogonal Greedy Algorithms that con…
On the generalized Approximate Weak Chebyshev Greedy Algorithm
Anton Dereventsov
In this paper we study greedy approximation in Banach spaces. We discuss a modification of the Weak Chebyshev Greedy Algorithm, in which steps of the algorithm can be executed impr…
On the Approximate Weak Chebyshev Greedy Algorithm in Uniformly Smooth Banach Spaces
A. V. Dereventsov
We study greedy approximation in uniformly smooth Banach spaces. The Weak Chebyshev Greedy Algorithm (WCGA) is defined for any Banach space and a dictionary , and…