94 citations · 153 across the 7 of their papers we have counts for
7 papers
A Reinforcement Learning Approach to Estimating Long-term Treatment Effects
Ziyang Tang, Yiheng Duan, Stephanie Zhang +1
Randomized experiments (a.k.a. A/B tests) are a powerful tool for estimating treatment effects, to inform decisions making in business, healthcare and other applications. In many p…
Split Localized Conformal Prediction
Xing Han, Ziyang Tang, Joydeep Ghosh +1
Conformal prediction is a simple and powerful tool that can quantify uncertainty without any distributional assumptions. Many existing methods only address the average coverage gua…
Robust Imitation Learning from Corrupted Demonstrations
Liu Liu, Ziyang Tang, Lanqing Li +1
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers. Classical approaches such as Behavio…
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning
Ziyang Tang, Yihao Feng, Qiang Liu
Reinforcement learning (RL) has drawn increasing interests in recent years due to its tremendous success in various applications. However, standard RL algorithms can only be applie…
Stein Variational Gradient Descent With Matrix-Valued Kernels
Dilin Wang, Ziyang Tang, Chandrajit Bajaj +1
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference. In this work, we enhance…
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Ziyang Tang, Yihao Feng, Lihong Li +2
Infinite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al…
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
Qiang Liu, Lihong Li, Ziyang Tang +1
We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy. Importance sampling (IS)…