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cs.LG2024★ 1 cited
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
Haque Ishfaq, Yixin Tan, Yu Yang +5
Thompson sampling (TS) is one of the most popular exploration techniques in reinforcement learning (RL). However, most TS algorithms with theoretical guarantees are difficult to im…
cs.LG2022★ 4 cited
Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, Yixin Tan
Score-based generative modeling (SGM) has grown to be a hugely successful method for learning to generate samples from complex data distributions such as that of images and audio.…
cs.LG2022★ 8 cited
Convergence for score-based generative modeling with polynomial complexity
Holden Lee, Jianfeng Lu, Yixin Tan
Score-based generative modeling (SGM) is a highly successful approach for learning a probability distribution from data and generating further samples. We prove the first polynomia…