32 citations · 62 across the 50 of their papers we have counts for
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stat.ML2026
Beyond identifiability: Learning causal representations with few environments and finite samples
Inbeom Lee, Tongtong Jin, Bryon Aragam
We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide…
stat.ML2019
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Xinjie Fan, Yizhe Zhang, Zhendong Wang +1
Sequence generation models are commonly refined with reinforcement learning over user-defined metrics. However, high gradient variance hinders the practical use of this method. To…
stat.ML2019
Thompson Sampling via Local Uncertainty
Zhendong Wang, Mingyuan Zhou
Thompson sampling is an efficient algorithm for sequential decision making, which exploits the posterior uncertainty to address the exploration-exploitation dilemma. There has been…