Tongzheng Ren

About me

I am a third year PhD student in the Department of Computer Science at the University of Texas at Austin. I am fortunate to be advised by Prof. Sujay Sanghavi and I have close collaboration with Prof. Nhat Ho. I also worked with Prof. Qiang Liu during my first year at UT. My research focus on the theoretical aspects of machine learning, including but not restricted to optimization, statistics and online learning.

Starting from October 2021, I also work as a student researcher in Google Brain, hosted by Dr. Bo Dai.

Before moving to Austin, I received the B.S. degree in Fundamental Science and Double B.S. degree in Mathematics and Applied Mathematics from Tsinghua University in 2018. During the undergraduate study, I was fortunate to work with Prof. Jun Zhu on different topics in Machine Learning. See my CV for detailed information.

Publications

Peer Reviewed Conference

  • A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning [arXiv]
    Tongzheng Ren*, Tianjun Zhang*, Csaba SzepesvĂ¡ri, Bo Dai (* Equal Contribution)
    Conference on Uncertainty in Artifical Intelligence (UAI) 2022

  • Making Linear MDP Practical via Contrastive Representation Learning
    Tianjun Zhang*, Tongzheng Ren*, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai
    International Conference on Machine Learning (ICML) 2022

  • Linear Bandit Algorithms with Sublinear Time Complexity [arXiv]
    Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi
    International Conference on Machine Learning (ICML) 2022

  • Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent [arXiv]
    Tongzheng Ren*, Fuheng Cui*, Alexia Atsidakou*, Sujay Sanghavi, Nhat Ho (* Equal Contribution)
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2022

  • Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model
    Jialian Li, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu
    AAAI Conference on Artificial Intelligence (AAAI) 2022

  • Scalable Quasi-Bayesian Inference for Instrumental Variable Regression [arXiv]
    Ziyu Wang*, Yuhao Zhou*, Tongzheng Ren, Jun Zhu (* Equal Contribution)
    Advances in Neural Information Processing Systems (NeurIPS) 2021

  • Nearly Horizon-Free Offline Reinforcement Learning. [arXiv]
    Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi
    Advances in Neural Information Processing Systems (NeurIPS) 2021

  • Unsupervised Out-of-Domain Detection via Pre-trained Transformers. [arXiv]
    Keyang Xu, Tongzheng Ren, Shikun Zhang, Yihao Feng, Caiming Xiong
    The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP) 2021

  • MaxUp: A Simple Way to Improve Generalization of Neural Network Training. [arXiv]
    Chengyue Gong*, Tongzheng Ren*, Mao Ye, Qiang Liu (* Equal Contribution)
    IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) 2021

  • Learning Task-Distribution Reward-Shaping with Meta-Learning. [arXiv] [AAAI version]
    Haosheng Zou*, Tongzheng Ren*, Dong Yan, Hang Su, Jun Zhu (* Equal Contribution)
    AAAI Conference on Artificial Intelligence (AAAI) 2021

  • Stein Self-Repulsive Dynamics: Benefits from Past Samples. [arXiv] [NeurIPS version]
    Mao Ye*, Tongzheng Ren*, Qiang Liu (* Equal Contribution)
    Advances in Neural Information Processing Systems (NeurIPS) 2020

  • Implicit Regularization and Convergence for Weight Normalization. [arXiv] [NeurIPS version]
    Xiaoxia Wu*, Edgar Dobriban*, Tongzheng Ren*, Shanshan Wu*, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu (* Equal Contribution)
    Advances in Neural Information Processing Systems (NeurIPS) 2020

  • Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics. [arXiv] [ICML version]
    Yihao Feng*, Tongzheng Ren*, Ziyang Tang*, Qiang Liu (* Equal Contribution)
    International Conference on Machine Learning (ICML) 2020

  • Exploration Analysis in Finite-Horizon Turn-based Stochastic Games. [UAI version]
    Jialian Li, Yichi Zhou, Tongzheng Ren, Jun Zhu
    Conference on Uncertainty in Artificial Intelligence (UAI) 2020.

  • Lazy-CFR: a fast regret minimization algorithm for extensive games with imperfect information. [arXiv] [ICLR version]
    Yichi Zhou, Tongzheng Ren, Jialian Li, Dong Yan, Jun Zhu
    International Conference on Learning Representation (ICLR) 2020

  • Learn a Robust Policy in Adversarial Games via Playing with an Expert Opponent. [AAMAS version]
    Jialian Li, Tongzheng Ren, Hang Su, Jun Zhu
    International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) 2019 (Extended Abstract)

  • Function Space Particle Optimization for Bayesian Neural Networks. [arXiv] [ICLR version]
    Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang
    International Conference on Learning Representation (ICLR) 2019

  • Learning to write stylized Chinese Charaters by Reading a Handful of Examples. [arXiv] [IJCAI version]
    Danyang Sun*. Tongzheng Ren*, Chongxuan Li, Hang Su, Jun Zhu (* Equal Contribution)
    International Joint Conference on Artificial Intelligence (IJCAI) 2018

Preprint

  • Improving Computational Complexity in Statistical Models with Second-Order Information [arxiv]
    Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho

  • Combinatorial Bandits without Total Order for Arms. [arXiv]
    Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon, Sujay Sanghavi

Professional Activities

  • Conference Review: ICML, NeurIPS, ICLR, AAAI, AISTATS

  • Journal Review: JMLR, TNNLS