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Found 12 papers, 10 papers with code
An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner's Guide
Due to rapid advancements in the development of Large Language Models (LLMs), programming these models with prompts has recently gained significant attention.
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks
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
We present a method for zero-shot recommendation of multimodal non-stationary content that leverages recent advancements in the field of generative AI.
Modeling Non-deterministic Human Behaviors in Discrete Food Choices
We establish a non-deterministic model that predicts a user's food preferences from their demographic information.
Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
This effort is focused on examining the behavior of reinforcement learning systems in personalization environments and detailing the differences in policy entropy associated with the type of learning algorithm utilized.
Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets
We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, Last. fm, Million Song, etc.
On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks
In this effort we consider a reinforcement learning (RL) technique for solving personalization tasks with complex reward signals.
Offline Policy Comparison under Limited Historical Agent-Environment Interactions
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, or security considerations.
An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization
In this work, we propose a novel adaptive stochastic gradient-free (ASGF) approach for solving high-dimensional nonconvex optimization problems based on function evaluations.
Biorthogonal Greedy Algorithms in Convex Optimization
We show that the following well-known algorithms for convex optimization -- the Weak Chebyshev Greedy Algorithm (co) and the Weak Greedy Algorithm with Free Relaxation (co) -- belong to this class, and introduce a new algorithm -- the Rescaled Weak Relaxed Greedy Algorithm (co).
Numerical Analysis Numerical Analysis Optimization and Control
Neural network integral representations with the ReLU activation function
In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function.
Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks
We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function.
