Search Results for author: Miao Lu

Found 9 papers, 2 papers with code

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage

no code implementations16 May 2023 Jose Blanchet, Miao Lu, Tong Zhang, Han Zhong

We study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal robust policy purely from an offline dataset that can perform well in perturbed environments.

Offline RL

Robust Consensus Clustering and its Applications for Advertising Forecasting

no code implementations27 Dec 2022 Deguang Kong, Miao Lu, Konstantin Shmakov, Jian Yang

Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions.

Video Background Music Generation: Dataset, Method and Evaluation

no code implementations21 Nov 2022 Le Zhuo, Zhaokai Wang, Baisen Wang, Yue Liao, Stanley Peng, Chenxi Bao, Miao Lu, Xiaobo Li, Si Liu

To close this gap, we introduce a dataset, benchmark model, and evaluation metric for video background music generation.

Music Generation Representation Learning +1

Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach

no code implementations12 Sep 2022 Miao Lu, Wenhao Yang, Liangyu Zhang, Zhihua Zhang

Specifically, we propose a two-stage estimator based on the instrumental variables and establish its statistical properties in the confounded MDPs with a linear structure.

Off-policy evaluation

Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization

no code implementations20 Dec 2021 Yufei Kuang, Miao Lu, Jie Wang, Qi Zhou, Bin Li, Houqiang Li

Many existing algorithms learn robust policies by modeling the disturbance and applying it to source environments during training, which usually requires prior knowledge about the disturbance and control of simulators.

Bayesian Time Series Forecasting with Change Point and Anomaly Detection

no code implementations ICLR 2018 Anderson Y. Zhang, Miao Lu, Deguang Kong, Jimmy Yang

However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods.

Anomaly Detection Change Point Detection +2

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