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1。奖励在测试时间扩散模型中的奖励引入了迭代改进，适用于蛋白质和DNA设计Masatoshi uehara，Xingyu SU，Yulai Zhao，Yulai Zhao，Xiner LI，Aviv Regev，Shuiwang Ji，Sergey Ji，Sergey Levine，Sergey Levine，Tommaso Biancalani Arxiv Arxiv Preprint 2。与奖励指导一代的扩散模型中的推理时间对齐：教程和评论Masatoshi uehara，Yulai Zhao，Chenyu Wang，Xiner LI，Aviv Regev，Sergey Legev，Sergey Legev，Tommaso Biancalani Arxiv Arxiv Arxiv Preprint 3。Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding Xiner Li, Yulai Zhao , Chenyu Wang, Gabriele Scalia, Gokcen Eraslan, Surag Nair, Tommaso Biancalani, Shuiwang Ji, Aviv Regev, Sergey Levine, Masatoshi Uehara arXiv preprint 4.理解基于扩散模型的基于增强学习的微调：教程和评论Masatoshi uehara *，Yulai Zhao *，Tommaso Biancalani，Sergey Levine Arxiv Preprint 5。连续时间扩散模型的微调作为熵调查的对照果片uehara *，Yulai Zhao *，Kevin Black，Kevin Black，Ehsan Hajiramezanali，Gabriele Scalia，Nathaniel Lee Diemant，Alex M Tseng，Alex M Tseng，Tommaso Biancalani，Sergey/Sergey Levine在弱凸度假设下优化表现风险Yulai Zhao Neurips 2022关于机器学习优化的研讨会
elay Zhao
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