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PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators
Dongxia Wu, Mingyu Li, Yuhui Zhang +4
Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. Recent advances in flow-matching have enable…
V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think
Bingda Tang, Yuhui Zhang, Xiaohan Wang +3
Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement learning (RL) offers a princip…
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Dongxia Wu, Shiye Su, Yuhui Zhang +4
Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-base…
Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
Dongxia Wu, Yuhui Zhang, Serena Yeung-Levy +2
Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating medical images across condition…
Robust VAEs via Generating Process of Noise Augmented Data
Hiroo Irobe, Wataru Aoki, Kimihiro Yamazaki +5
Advancing defensive mechanisms against adversarial attacks in generative models is a critical research topic in machine learning. Our study focuses on a specific type of generative…