1 citations · 1 across the 3 of their papers we have counts for
4 papers
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
Masatoshi Uehara, Xingyu Su, Yulai Zhao +5
To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-gui…
Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review
Masatoshi Uehara, Yulai Zhao, Chenyu Wang +4
This tutorial provides an in-depth guide on inference-time guidance and alignment methods for optimizing downstream reward functions in diffusion models. While diffusion models are…
Provably Efficient CVaR RL in Low-rank MDPs
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu +4
We study risk-sensitive Reinforcement Learning (RL), where we aim to maximize the Conditional Value at Risk (CVaR) with a fixed risk tolerance . Prior theoretical work studying…
Optimizing the Performative Risk under Weak Convexity Assumptions
Yulai Zhao
In performative prediction, a predictive model impacts the distribution that generates future data, a phenomenon that is being ignored in classical supervised learning. In this clo…