10 citations · 12 across the 12 of their papers we have counts for
12 papers
Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs
Dom Huh, Prasant Mohapatra
Language is a ubiquitous tool that is foundational to reasoning and collaboration, ranging from everyday interactions to sophisticated problem-solving tasks. The establishment of a…
Multi-agent Auto-Bidding with Latent Graph Diffusion Models
Dom Huh, Prasant Mohapatra
This paper proposes a diffusion-based auto-bidding framework that leverages graph representations to model large-scale auction environments. In such settings, agents must dynamical…
Maximize Your Diffusion: A Study into Reward Maximization and Alignment for Diffusion-based Control
Dom Huh, Prasant Mohapatra
Diffusion-based planning, learning, and control methods present a promising branch of powerful and expressive decision-making solutions. Given the growing interest, such methods ha…
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning
Dom Huh, Prasant Mohapatra
Sample efficiency remains a key challenge in multi-agent reinforcement learning (MARL). A promising approach is to learn a meaningful latent representation space through auxiliary…
Reinforcement Learning Problem Solving with Large Language Models
Sina Gholamian, Domingo Huh
Large Language Models (LLMs) encapsulate an extensive amount of world knowledge, and this has enabled their application in various domains to improve the performance of a variety o…
Multi-agent Reinforcement Learning: A Comprehensive Survey
Dom Huh, Prasant Mohapatra
Multi-agent systems (MAS) are widely prevalent and crucially important in numerous real-world applications, where multiple agents must make decisions to achieve their objectives in…
Decentralized Multi-agent Filtering
Dom Huh, Prasant Mohapatra
This paper addresses the considerations that comes along with adopting decentralized communication for multi-agent localization applications in discrete state spaces. In this frame…
Mix and Mask Actor-Critic Methods
Dom Huh
Shared feature spaces for actor-critic methods aims to capture generalized latent representations to be used by the policy and value function with the hopes for a more stable and s…
Synthetic Embedding-based Data Generation Methods for Student Performance
Dom Huh
Given the inherent class imbalance issue within student performance datasets, samples belonging to the edges of the target class distribution pose a challenge for predictive machin…
Greedy Bandits with Sampled Context
Dom Huh
Bayesian strategies for contextual bandits have proved promising in single-state reinforcement learning tasks by modeling uncertainty using context information from the environment…
Hierarchical BiGraph Neural Network as Recommendation Systems
Dom Huh
Graph neural networks emerge as a promising modeling method for applications dealing with datasets that are best represented in the graph domain. In specific, developing recommenda…
Generative Multi-Stream Architecture For American Sign Language Recognition
Dom Huh, Sai Gurrapu, Frederick Olson +3
With advancements in deep model architectures, tasks in computer vision can reach optimal convergence provided proper data preprocessing and model parameter initialization. However…