Portrait of Wei Chen

Wei Chen

Principal Research Manager

About

Wei Chen (陈薇) is a principal research manager in Microsoft Research Asia. She is leading the Computing and Learning Theory Group, to push the frontiers of learning and computing in AI through theoretical understandings. Her current research interests include deep learning theory, deep reinforcement learning, differential privacy in learning, causality, and game theory.  Before joined Microsoft, she obtained her Ph.D. in Mathematics from Chinese Academy of Sciences, advised by Prof. Zhi-Ming Ma. Now, she is an adjunct professor in the University of Science and Technology of China, Beijing Jiaotong University, and Nankai University.

Publications:

Book (in Chinese): Tie-Yan Liu, Wei Chen, Taifeng Wang, Fei Gao,  Distributed Machine Learning:  Algorithms, Theory, and Practice, China Machine Press, Beijing, 2018 (ISBN 978-7-111-60918-6)

Conference/Journal Papers:

  • Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu, Machine-Learning Non-conservative Dynamics for New-Physics Detection, accepted by Physical Review E, 2021
  • Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu, Optimizing Information-theoretic Generalization Bound via Anisotropic Noise of SGLD, NeurIPS, 2021
  • Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, and Tie-Yan Liu, Recovering Latent Causal Factor for Generalization to Distributional Shifts, NeurIPS, 2021
  • Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu, Learning Causal Semantic Representation for out-of-distribution Prediction, NeurIPS, 2021
  • Xiaobo liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu, R-Drop: Regularized Dropout for Neural Networks, NeurIPS, 2021
  • Bohan Wang, Qi Meng, Wei Chen, and Tie-Yan Liu, The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks, In Proceedings of the  38th International Conference on Machine Learning (ICML) with long presentation, 2021.
  • Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu, Large Scale Private Learning via Low-rank Reparametrization, In Proceedings of the 38th International Conference on Machine Learning  (ICML), 2021.
  • Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, and Tie-Yan Liu, Path-BN: Towards Effective Batch Normalization in the Path Space for ReLU Networks, In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021
  • Juanping Zhu, Qi Meng, Wei Chen, Yue Wang, and Zhi-Ming Ma, Interpreting the Basis Path Set in Neural Networks, Journal of System Science and Complexity, 2021.
  • Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu, Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning,  In Proceedings of the 9th International Conference of Learning Representations (ICLR), 2021
  • Huishuai Zhang, Da Yu, Mingyang Yi, Wei Chen, and Tie-Yan Liu, Convergence Theory of Learning Over-parameterized ResNet: A Full Characterization, https://arxiv.org/abs/1903.07120
  • Qi Meng, Shiqi Gong, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, Dynamics of SGD with State-dependent Noise,  https://arxiv.org/abs/2006.13719 
  • Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu, How Does Data Augmentation Affect Privacy in Machine Learning? In Proceedings of the 35th International Association for the Advancement of Artificial Intelligence Conference (AAAI), 2021.
  • Juanping Zhu, Qi Meng, Wei Chen, and Zhi-Ming Ma, Interpreting the Basis Path Set in Neural Networks, Journal of System Science and Complexity, Vol(33):1-13, 2020.
  • Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu, Gradient Perturbation is Underrated for Differentially Private Convex Optimization, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2020.
  • Xufang Luo, Qi Meng, Di He, Wei Chen, and Yonghong Wang, I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2020.
  • Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, and Longbo Huang, Reinforcement Learning with Dynamic Boltzmann Softmax Updates, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2020.
  • Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, and Tie-Yan Liu, Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data, IEEE Transactions on Signal Processing, 2020
  • Mingyang Yi, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, BN-invariance Sharpness Regularizes the Training Model to Better Generalization, In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), 2019.
  • Qi Meng*, Shuxin Zheng*, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space, In Proceedings of the 7th International Conference of Learning Representations (ICLR), 2019,Code
  • Li He, Shuxin Zheng, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, OptQuant: Distributed Training of Neural Networks with Optimized Quantization Mechanism, NeuroComputing, 2019.
  • Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, and Tie-Yan Liu, Capacity Control of ReLU Neural Networks by Basis-path Norm, In Proceedings of the 33rd International Association for the Advancement of Artificial Intelligence Conference (AAAI), 2019.
  • Qi Meng, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling, NeuroComputing, 2019.
  • Huishuai Zhang, Wei Chen, and Tie-Yan Liu, On the Local Hessian in Back-propagation, In Advances in Neural Information Processing Systems 32 (NeurIPS), 2018.
  • Da Yu, Huishuai Zhang, and Wei Chen, Improving Gradient Perturbation for Differentially Private Optimization, NeurIPS Workshop on Privacy Preserving Machine Learning, 2018.
  • Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, and Tie-Yan Liu, Towards Binary-Valued Gates for Robust LSTM Training, In Proceeding of the 35th International Conference on Machine LearningICML), 2018.
  • Li He, Qi Meng, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, Differential Equations for Modeling Asynchronous Algorithms, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2018.
  • Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, and Tie-Yan Liu, Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning, In Proceeding of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018
  • Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, and Tie-Yan Liu, Finite Sample Analysis of GTD Policy Evaluation Algorithms in Markov Setting, In Advances in Neural Information Processing Systems 31 (NeurIPS), 2017
  • Guolin Ke, Qi Meng, Taifeng Wang, Wei Chen, Weidong Ma, Tie-Yan Liu, LightGBM: A Highly Efficient Gradient Boosting Decision Tree,  In Advances in Neural Information Processing Systems 31 (NeurIPS), 2017.
  • Yingce Xia, Tao Qin, Wei Chen and Tie-Yan Liu, Dual Supervised Learning, In Proceeding of the 34th International Conference on Machine Learning (ICML), 2017
  • Shuxin Zheng*, Qi Meng*, Taifeng Wang, Wei Chen, Zhi-Ming Ma and Tie-Yan Liu, Asynchronous Stochastic Gradient Descent with Delay Compensation, In Proceeding of the 34thInternational Conference on Machine Learning (ICML), 2017
  • Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu and Tie-Yan Liu, Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks, European Conference on Machine Learning (ECML), 2017.
  • Quanming Yao, James Kwok, Fei Gao, Wei Chen, and Tie-Yan Liu, Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
  • Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction, Proceedings of the 31st International Association for the Advancement of Artificial Intelligence Conference (AAAI), 2017.
  • Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Generalization Error Bounds for Optimization Algorithms via StabilityProceedings of the 31st International Association for the Advancement of Artificial Intelligence Conference (AAAI), 2017.
  • Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, and Tie-Yan Liu, A Communication-efficient Parallel Algorithms for Decision Tree, Advances in Neural Information Processing Systems 30 (NeurIPS), 2016.
  • Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma, and Tie-Yan Liu, Asynchronous Accelerated Stochastic Gradient Descent, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2016.
  • Wei Chen, Tie-Yan Liu, and Xinxin Yang, Reinforcement Learning Behaviors in Sponsored Search, Applied Stochastic Models in Business and Industry (ASMB),2016.
  • Shizhao Sun, Wei Chen, Liwei Wang, Xiaoguang Liu, and Tie-Yan Liu, On the Depth of Deep Neural Networks: A Theoretical View, In Proceedings of the 30th International Association for the Advancement of Artificial Intelligence Conference (AAAI), 2016.
  • Wei Chen, Tao Qin, Weidong Ma, and Tie-Yan Liu. Advances in Ad Auction Mechanism Design for Sponsored Search. Book chapter in Multi-Agent System and its Applications. Tsinghua University Press, Beijing, 2015. (ISBN 978-7-302-40252-7, in Chinese)
  • Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, Senior Member Track, In Proceedings of the 29th International Association for the Advancement of Artificial Intelligence Conference (AAAI), 2015.
  • Haifang Li, Tian Fei, Wei Chen, Tao Qin, Zhiming Ma, and Tie-Yan Liu, Generalization Analysis for Game-Theoretic Machine Learning, Proceedings of the 29th International Association for the Advancement of Artificial Intelligence Conference (AAAI), 2015.
  • Tao Qin, Wei Chen, and Tie-Yan Liu, Sponsored Search Auctions: Recent Advances and Future Directions, ACM Transactions on Intelligent Systems and Technology (TIST),5 (4), 2014.
  • Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, and Liwei Wang, Generalized Second Price Auction with Probabilistic Broad Match, In Proceedings of the 15th ACM Conference on Economics and Computation (EC), Pages 39-56, 2014.
  • Fei Tian, Haifang Li, Wei Chen, Tao Qin and Tie-Yan Liu, Agent Behavior Prediction and Its Generalization Analysis, In Proceedings of the 28th International Association for the Advancement of Artificial Intelligence Conference (AAAI), Pages 1300-1306, 2014.
  • Jun Feng, Jiang Bian, Taifeng Wang, Wei Chen, Xiaoyan Zhu and Tie-Yan Liu, Sampling Dilemma: Towards Effective Data Sampling for Click Prediction in Sponsored Search, In Proceedings of 7th ACM Conference on Web Search and Data Mining (WSDM), Pages 130-112, 2014.
  • Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Online Learning for Auction Mechanism in Bandit Setting, Decision Support Systems (DSS), Vol 56, Pages 379-386, 2013.
  • Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Probabilistic Broad Match for Sponsored Search Auctions, EC AdAuction Workshop 2013.
  • Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu, A Theoretical Analysis of NDCG Type Ranking Measures, In Proceedings of the 26th Annual Conference on Learning Theory (COLT), Pages 25-54,2013
  • Di He, Wei Chen, Liwei Wang, Tie-Yan Liu, A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search, In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), Pages 206-212, 2013.
  • Lei Yao, Wei Chen, Tie-Yan Liu. Convergence Analysis for Weighted Joint Strategy Fictitious Play in Generalized Second Price Auction, Proceedings of the 10th Conference on Web and Internet Economics (WINE), Pages 489-495, 2012.
  • Wei Chen, Tie-Yan Liu, and Zhi-Ming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, Advances in Neural Information Processing Systems 23 (NeurIPS), Pages 370-378, 2010.
  • Wei Chen, Tie-Yan Liu, Yanyan Lan, and Zhi-Ming Ma, Ranking Measures and Loss functions in Learning to Rank, Advances in Neural Information Processing Systems 22 (NeurIPS), Pages 315-323, 2009.