14 citations · 28 across the 7 of their papers we have counts for
7 papers
Diffusion Large Language Models for Black-Box Optimization
Ye Yuan, Can, Chen +4
Offline black-box optimization (BBO) aims to find optimal designs based solely on an offline dataset of designs and their labels. Such scenarios frequently arise in domains like DN…
Generative Point Tracking with Flow Matching
Mattie Tesfaldet, Adam W. Harley, Konstantinos G. Derpanis +2
Tracking a point through a video can be a challenging task due to uncertainty arising from visual obfuscations, such as appearance changes and occlusions. Although current state-of…
ParetoFlow: Guided Flows in Multi-Objective Optimization
Ye Yuan, Can Chen, Christopher Pal +1
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minimize multiple objectives. This setting mo…
Robust Guided Diffusion for Offline Black-Box Optimization
Can Sam Chen, Christopher Beckham, Zixuan Liu +2
Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two main approaches have emerged: the forwar…
Parallel-mentoring for Offline Model-based Optimization
Can Chen, Christopher Beckham, Zixuan Liu +2
We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores. These designs encompass a variety of domains, incl…
Real-Time Reinforcement Learning
Simon Ramstedt, Christopher Pal
Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the sta…
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Julien Roy, Paul Barde, Félix G. Harvey +2
In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the nu…