Bio. I am a Principal Scientist at Sea AI Lab, and an adjunct assistant professor at the National University of Singapore, focusing on Deep Reinforcement Learning. I was a senior research scientist at DeepMind before joining Sea.
I received my Ph.D. degree from University of Technology Sydney (UTS), advised by Prof. Yi Yang. I received my Bachelor's degree from Zhejiang University in 2013, under the supervision of Prof. Yueting Zhuang and Prof. Fei Wu.
During my studies, I was fortunate to spend time at Carnegie Mellon University, National University of Singapore, Google Brain, and DeepMind.
Learning to Optimize for Reinforcement Learning
Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu
arXiv
Reinforcement Learning from Diverse Human Preferences
Wanqi Xue, Bo An, Shuicheng Yan, Zhongwen Xu
arXiv
Mutual Information Regularized Offline Reinforcement Learning
Xiao Ma, Bingyi Kang, Zhongwen Xu, Min Lin, Shuicheng Yan
arXiv
Boosting Offline Reinforcement Learning via Data Rebalancing
Yang Yue, Bingyi Kang, Xiao Ma, Zhongwen Xu, Gao Huang, Shuicheng Yan
arXiv
Benchmarking Deformable Object Manipulation with Differentiable Physics
Siwei Chen, Cunjun Yu, Yiqing Xu, Linfeng Li, Xiao Ma, Zhongwen Xu, David Hsu
ICLR 2023 notable-top-5% (Oral) | GitHub
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning
Wei Qiu, Xiao Ma, Bo An, Svetlana Obraztsova, Shuicheng Yan, Zhongwen Xu
ICLR 2023
Visual Imitation Learning with Patch Rewards
Minghuan Liu, Tairan He, Weinan Zhang, Shuicheng YAN, Zhongwen Xu
ICLR 2023
Distributional Meta-Gradient Reinforcement Learning
Haiyan Yin, Shuicheng YAN, Zhongwen Xu
ICLR 2023
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Yang Yue, Bingyi Kang, Zhongwen Xu, Gao Huang, Shuicheng Yan
AAAI 2023
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, Shuicheng Yan
NeurIPS 2022 Datasets and Benchmarks | GitHub
Discovery of Options via Meta-Learned Subgoals
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
NeurIPS 2021
ETA Prediction with Graph Neural Networks in Google Maps
Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser,Seongjae Lee,
Xueying Guo, Brett Wiltshire, Peter W Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Velickovic
CIKM 2021 | DeepMind Blog
Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
ICML 2021
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
NeurIPS 2020
Balancing Constraints and Rewards with Meta-Gradient D4PG
Dan A Calian, Daniel J Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy Mann
ICLR 2021
Discovering Reinforcement Learning Algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
NeurIPS 2020
A Self-Tuning Actor-Critic Algorithm
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
NeurIPS 2020
What Can Learned Intrinsic Rewards Capture?
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
ICML 2020
Discovery of Useful Questions as Auxiliary Tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
NeurIPS 2019
Meta-Gradient Reinforcement Learning
Zhongwen Xu, Hado van Hasselt, and David Silver
NeurIPS 2018
Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification
Hehe Fan, Zhongwen Xu, Linchao Zhu, Chenggang Yan, Jianjun Ge, and Yi Yang
IJCAI 2018
Natural Value Approximators: Learning When to Trust Past Estimates.
Zhongwen Xu, Joseph Modayil, Hado van Hasselt, Andre Barreto, David Silver and Tom Schaul
NIPS 2017 (Spotlight)
Few-Shot Object Recognition from Machine-Labeled Web Images
Zhongwen Xu*, Linchao Zhu* and Yi Yang
CVPR 2017 (Spotlight)
[Code] [Spotlight Video]
Bidirectional Multirate Reconstruction for Temporal Modeling in Videos
Linchao Zhu, Zhongwen Xu and Yi Yang
CVPR 2017 (Spotlight)
[Code]
An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning
Fan Wu, Zhongwen Xu and Yi Yang
arXiv preprint 1703.07579 [Code]
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning
Pingbo Pan, Zhongwen Xu, Yi Yang, Fei Wu and Yueting Zhuang
CVPR 2016 [PDF]
Robust Semi-supervised Learning through Label Aggregation
Yan Yan, Zhongwen Xu, Ivor W. Tsang, Guodong Long and Yi Yang
AAAI 2016 [PDF]
A Discriminative CNN Video Representation for Event Detection
Zhongwen Xu, Yi Yang and Alexander G. Hauptmann
CVPR 2015 [PDF]
Content-Based Video Search over 1 Million Videos with 1 Core in 1 Second
Shoou-I Yu, Lu Jiang, Zhongwen Xu, Yi Yang and Alexander G. Hauptmann
ICMR 2015
Event Detection Using Multi-level Relevance Labels and Multiple Features
Zhongwen Xu, Ivor W. Tsang, Yi Yang, Zhigang Ma and Alexander G. Hauptmann
CVPR 2014 [PDF]
Feature Weighting via Optimal Thresholding for Video Analysis
Zhongwen Xu, Yi Yang, Ivor W. Tsang, Nicu Sebe and Alexander G. Hauptmann
ICCV 2013 [PDF]
How Related Exemplars Help Complex Event Detection in Web Videos?
Yi Yang, Zhigang Ma, Zhongwen Xu, Shuicheng Yan and Alexander G. Hauptmann
ICCV 2013 [PDF]
Complex Event Detection via Multi-source Video Attributes
Zhigang Ma, Yi Yang, Zhongwen Xu, Shuicheng Yan, Nicu Sebe and Alexander G. Hauptmann
CVPR 2013 [PDF]
We Are Not Equally Negative: Fine-grained Labeling for Multimedia Event Detection
Zhigang Ma, Yi Yang, Zhongwen Xu, Nicu Sebe and Alexander G. Hauptmann
ACM Multimedia 2013 [PDF]
Meta-gradient updates for training return functions for reinforcement learning systems
Zhongwen Xu, Hado van Hasselt, David Silver
US Patent App. 16/417,536
Training action selection neural networks using a differentiable credit function
Zhongwen Xu, Hado van Hasselt, Joseph Modayil, Andre Barreto, David Silver
US Patent App. 16/615,042
UTS-CMU at THUMOS 2015
Zhongwen Xu, Linchao Zhu, Yi Yang and Alexander G. Hauptmann
THUMOS challenge 2015
[Ranked 1st place]
[PDF]
[THUMOS Challenge]
Cross-media Relevance Mining for Evaluating Text-based Image Search Engine
Zhongwen Xu, Yi Yang, Ashraf A. Kassim and Shuicheng Yan
ICME MSR-Bing Grand Challenge Workshop 2014
[Ranked 1st place]
[PDF]
[MSR-Bing Image Retrieval Challenge (IRC)]
Informedia@TRECVID 2014 MED and MER
Shoou-I Yu, Lu Jiang, Zhongwen Xu, et al. and Alexander G. Hauptmann
TRECVID Workshop 2014
[Ranked 1st place in MED]
[PDF]
[TRECVID Multimedia Event Detection 2014]
ILSVRC (ImageNet) 2014 Classification with Provided Data Only
Zhongwen Xu and Yi Yang
[Ranking: Google, VGG, MSRA, Howard, DeepVision, NUS, TTIC, XYZ (ours)]
[link]
Informedia E-Lamp @ TRECVID 2012 Multimedia Event Detection and Recounting (MED and MER)
Shoou-I Yu, Zhongwen Xu, Duo Ding, et al. and Alexander G. Hauptmann
TRECVID Workshop 2012
[Ranked 1st place in Pre-specific events, 2nd place in Ad-hoc events]
[PDF]
[TRECVID Multimedia Event Detection 2012]
Theme from Karen Simonyan.