13 citations · 26 across the 5 of their papers we have counts for
5 papers
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…
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
Xiuyuan Cheng, Jianfeng Lu, Yixin Tan +1
Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance. Compared to th…
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.…
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…
From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information
Yixin Tan, Xiaomeng Wang, Tao Jia
The hyponym-hypernym relation is an essential element in the semantic network. Identifying the hypernym from a definition is an important task in natural language processing and se…