34 citations · 108 across the 16 of their papers we have counts for
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cs.LG2019★ 34 cited
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
Chi Jin, Tiancheng Jin, Haipeng Luo +2
We consider the problem of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses. We propose an…
cs.LG2019★ 1 cited
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation
Tiancheng Yu, Suvrit Sra
A Markov Decision Process (MDP) is a popular model for reinforcement learning. However, its commonly used assumption of stationary dynamics and rewards is too stringent and fails t…
cs.LG2019★ 1 cited
Near Optimal Stratified Sampling
Tiancheng Yu, Xiyu Zhai, Suvrit Sra
The performance of a machine learning system is usually evaluated by using i.i.d.\ observations with true labels. However, acquiring ground truth labels is expensive, while obtaini…