1 citations · 1 across the 7 of their papers we have counts for
9 papers
Anisotropic Gaussian Smoothing for Gradient-based Optimization
Andrew Starnes, Guannan Zhang, Viktor Reshniak +1
This article introduces a novel family of optimization algorithms - Anisotropic Gaussian Smoothing Gradient Descent (AGS-GD), AGS-Stochastic Gradient Descent (AGS-SGD), and AGS-Ada…
Mamba for Scalable and Efficient Personalized Recommendations
Andrew Starnes, Clayton Webster
In this effort, we propose using the Mamba for handling tabular data in personalized recommendation systems. We present the \textit{FT-Mamba} (Feature Tokenizer\,\,Mamba), a nov…
Improved Performance of Stochastic Gradients with Gaussian Smoothing
Andrew Starnes, Clayton Webster
This paper formalizes and analyzes Gaussian smoothing applied to two prominent optimization methods: Stochastic Gradient Descent (GSmoothSGD) and Adam (GSmoothAdam) in deep learnin…
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.…
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…
Effect of Random Time Changes on Loewner Hulls
Kei Kobayashi, Joan Lind, Andrew Starnes
Loewner hulls are determined by their real-valued driving functions. We study the geometric effect on the Loewner hulls when the driving function is composed with a random time cha…
The Loewner Equation for Multiple Hulls
Andrew Starnes
Kager, Nienhuis, and Kadanoff conjectured that the hull generated from the Loewner equation driven by two constant functions with constant weights could be generated by a single ra…