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Howdy! I am a fourth-year PhD student from the Department of Computer Science at Rice University, working with Dr. Xia (Ben) Hu. Prior to Rice, I was a PhD student at Texas A&M University, supervised by Dr. Xia (Ben) Hu. I received my Bachelor degree in Computer Science from Wuhan University in 2018, working with Dr. Chenliang Li. In Summer 2021, I was a Research Intern at Facebook, working on automated machine learning and machine learning systems with Dr. Louis Feng. I was a Research Intern at Seattle AI Lab of Kuai Inc. in Summer 2020, working with Dr. Wenye Ma and Dr. Ji Liu.

My research mainly focuses on machine learning and data mining. In particular, I am interested in Automated Machine Learning (AutoML) and Reinforcement Learning (RL). I am also interested in their applications in Anomaly and Outlier Detection, Graph Neural Networks, Time-Series Analysis, Recommender Systems, and Machine Learning Systems, etc.

Open-Source Projects

RLCard: A Toolkit for Reinforcement Learning in Card Games

Presented in IJCAI 2020
GitHub Repo stars GitHub Repo forks  [Website] | [Paper] | [Demo] | [Code] | [Video]

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Presented in ICML 2021
GitHub Repo stars GitHub Repo forks  [Demo] | [Paper] | [Code]

AutoVideo: An Automated Video Action Recognition System (Newly Released)

Preprint
GitHub Repo stars GitHub Repo forks  [Paper] | [Code]

TODS: An Automated Time-series Outlier Detection System

Presented in AAAI 2021
GitHub Repo stars GitHub Repo forks  [Website] | [Paper] | [Code] | [Video]

PyODDS: An End-to-end Outlier Detection System

Presented in WWW 2020
GitHub Repo stars GitHub Repo forks  [Website] | [Paper] | [Code]

Publications

[Google Schorlar]
* Equal contribution

2021

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu
NeurIPS 2021, Neural Information Processing Systems

Revisiting Time Series Outlier Detection: Definitions and Benchmarks

Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, Xia Hu
NeurIPS 2021, Neural Information Processing Systems, Datasets and Benchmarks Track
[PDF] | [Code]

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu
ICML 2021, International Conference on Machine Learning
[PDF] | [Code] | [Demo] | [Slide]

AutoAD: Automated Anomaly Detection via Curiosity-guided Search and Self-imitation Learning

Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, Xia Hu
TNNLS 2021, IEEE Transactions on Neural Networks and Learning Systems
[PDF]

Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments

Daochen Zha, Wenye Ma, Lei Yuan, Xia Hu, Ji Liu
ICLR 2021, International Conference on Learning Representations
[PDF] | [Slide] | [Code]

AutoOD: Neural Architecture Search for Outlier Detection

Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu
ICDE 2021, IEEE International Conference on Data Engineering
[PDF]

TODS: An Automated Time Series Outlier Detection System

Kwei-Herng Lai*, Daochen Zha*, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Mingyang Wan, Diego Martinez, Xia Hu
AAAI 2021, AAAI Conference on Artificial Intelligence, demo track
[Paper] | [Code] | [Video]

2020

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu
NeurIPS 2020, Neural Information Processing Systems
[PDF] | [Code]

Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

Daochen Zha, Kwei-Herng Lai, Mingyang Wan, and Xia Hu
ICDM 2020, IEEE International Conference on Data Mining
[PDF] | [Slide] | [Code]

PolicyGNN: Aggregation Optimization for Graph Neural Networks

Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, and Xia Hu
KDD 2020, ACM SIGKDD Conference on Knowledge Discovery and Data Mining
[PDF]

RLCard: A Platform for Reinforcement Learning in Card Games

Daochen Zha*, Kwei-Herng Lai*, Songyi Huang∗, Yuanpu Cao, Keerthana Reddy, Juan Vargas, Alex Nguyen, Ruzhe Wei, Junyu Guo, and Xia Hu
IJCAI 2020, International Joint Conference on Artificial Intelligence, demo track
[PDF] | [Code] | [Video]

Dual Policy Distillation

Daochen Zha*, Kwei-Herng Lai* Yuening Li, and Xia Hu
IJCAI 2020, International Joint Conference on Artificial Intelligence
[PDF] | [Code]

Multi-Channel Graph Neural Networks

Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu
IJCAI 2020, International Joint Conference on Artificial Intelligence
[PDF]

PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning

Yuening Li, Daochen Zha, Praveen Venugopal, Na Zou, and Xia Hu
WWW 2020 Web Conference, demo track
[PDF] | [Code]

RLCard: A Toolkit for Reinforcement Learning in Card Games

Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei, Junyu Guo, and Xia Hu
AAAI-Workshop 2020, AAAI-20 Workshop on Reinforcement Learning in Games
[PDF] | [Code] | [Video]

2019

Multi-Label Dataless Text Classification with Topic Modeling

Daochen Zha, and Chenliang Li
KAIS 2019, Knowledge and Information Systems Journal
[PDF] | [Code]

Experience Replay Optimization

Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, and Xia Hu
IJCAI 2019, International Joint Conferences on Artificial Intelligence
[PDF] | [Slide]