5 citations · 13 across the 7 of their papers we have counts for
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cs.LG2024★ 5 cited
Accelerating Convergence of Score-Based Diffusion Models, Provably
Gen Li, Yu Huang, Timofey Efimov +3
Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampli…
cs.LG2024★ 1 cited
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
Yuchen Wu, Minshuo Chen, Zihao Li +2
Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. Such information is coine…
cs.LG2022★ 3 cited
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Gen Li, Yuejie Chi, Yuting Wei +1
This paper studies multi-agent reinforcement learning in Markov games, with the goal of learning Nash equilibria or coarse correlated equilibria (CCE) sample-optimally. All prior r…