no code implementations • ICML 2020 • Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
In this work, we investigate the statistical properties of the kernel loss, which allows us to find a feasible set that contains the true value function with high probability.
no code implementations • 14 Oct 2022 • Ziyang Tang, Yiheng Duan, Stephanie Zhang, Lihong Li
Randomized experiments (a. k. a.
1 code implementation • 27 Jun 2022 • Xing Han, Ziyang Tang, Joydeep Ghosh, Qiang Liu
The modified score inherits the spirit of split conformal methods, which is simple and efficient and can scale to high dimensional settings.
no code implementations • 29 Jan 2022 • Liu Liu, Ziyang Tang, Lanqing Li, Dijun Luo
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers.
no code implementations • 1 Jan 2022 • Ziyang Tang, Yihao Feng, Qiang Liu
The benefit of learning the operator is that we can incorporate any new reward function as input and attain its corresponding value function in a zero-shot manner.
no code implementations • ICLR 2021 • Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies.
no code implementations • NeurIPS 2020 • Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data.
no code implementations • 15 Aug 2020 • Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
We consider off-policy evaluation (OPE), which evaluates the performance of a new policy from observed data collected from previous experiments, without requiring the execution of the new policy.
no code implementations • 22 Jan 2020 • Ziyang Tang, Xiang Liu, Guangyu Shen, Baijian Yang
Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities, and disaster assistance.
1 code implementation • NeurIPS 2019 • Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference.
no code implementations • ICLR 2020 • Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu
Our method is doubly robust in that the bias vanishes when either the density ratio or the value function estimation is perfect.
no code implementations • 3 Mar 2019 • Xiang Liu, Ziyang Tang, Huyunting Huang, Tonglin Zhang, Baijian Yang
Results showed our approaches can achieve closed-form solutions of multiple models at the cost of half training time of the traditional methods for a single model.
2 code implementations • NeurIPS 2018 • Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy.