4 citations · 12 across the 11 of their papers we have counts for
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Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design
Xingyu Su, Xiner Li, Masatoshi Uehara +7
We address the problem of fine-tuning diffusion models for reward-guided generation in biomolecular design. While diffusion models have proven highly effective in modeling complex,…
Dynamic Search for Inference-Time Alignment in Diffusion Models
Xiner Li, Masatoshi Uehara, Xingyu Su +5
Diffusion models have shown promising generative capabilities across diverse domains, yet aligning their outputs with desired reward functions remains a challenge, particularly in…
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…
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design
Chenyu Wang, Masatoshi Uehara, Yichun He +7
Recent studies have demonstrated the strong empirical performance of diffusion models on discrete sequences across domains from natural language to biological sequence generation.…
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding
Xiner Li, Yulai Zhao, Chenyu Wang +8
Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely generating designs that are natural…
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review
Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani +1
This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to optimize downstream reward functions. While diffusion models are widely known to provid…
Adding Conditional Control to Diffusion Models with Reinforcement Learning
Yulai Zhao, Masatoshi Uehara, Gabriele Scalia +4
Diffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples. While these diffusion models trained on large data…
Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models
Masatoshi Uehara, Yulai Zhao, Ehsan Hajiramezanali +5
AI-driven design problems, such as DNA/protein sequence design, are commonly tackled from two angles: generative modeling, which efficiently captures the feasible design space (e.g…
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
Masatoshi Uehara, Yulai Zhao, Kevin Black +6
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins. While diffusion models are trained to represent the distribution in th…
Feedback Efficient Online Fine-Tuning of Diffusion Models
Masatoshi Uehara, Yulai Zhao, Kevin Black +6
Diffusion models excel at modeling complex data distributions, including those of images, proteins, and small molecules. However, in many cases, our goal is to model parts of the d…
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel +1
While machine learning models are typically trained to solve prediction problems, we might often want to use them for optimization problems. For example, given a dataset of protein…