10 citations · 12 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…