no code implementations • 16 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.
no code implementations • 27 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.
no code implementations • 21 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.
no code implementations • 12 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.
no code implementations • 26 May 2022 • Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang
We study offline reinforcement learning (RL) in partially observable Markov decision processes.
1 code implementation • CVPR 2022 • Yue Liao, Aixi Zhang, Miao Lu, Yongliang Wang, Xiaobo Li, Si Liu
In this paper, we reveal and address the disadvantages of the conventional query-driven HOI detectors from the two aspects.
Ranked #6 on Human-Object Interaction Detection on HICO-DET
no code implementations • 20 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.
1 code implementation • NeurIPS 2021 • Aixi Zhang, Yue Liao, Si Liu, Miao Lu, Yongliang Wang, Chen Gao, Xiaobo Li
To this end, we propose a novel one-stage framework with disentangling human-object detection and interaction classification in a cascade manner.
Ranked #5 on Human-Object Interaction Detection on V-COCO
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.