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Tongzheng Ren
About me
I'm a Quantitative Researcher at Citadel Securities.
I obtained the PhD degree in Computer Science at the University of Texas at Austin.
I was fortunate to be advised by Prof. Sujay Sanghavi and I had 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.
In Summer 2023, I worked as a quantitative research intern at Citadel Securities.
From October 2021 to May 2023, I also worked as a student researcher in Google Brain (now Google DeepMind), hosted by Prof. 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
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning [arXiv] [ICML version]
Hongming Zhang*, Tongzheng Ren*, Chenjun Xiao, Dale Schuurmans, Bo Dai (* Equal Contribution)
International Conference on Machine Learning (ICML) 2024
Improving Computational Complexity in Statistical Models with Local Curvature Information [arXiv] [ICML version]
Pedram Akbarian*, Tongzheng Ren*, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho (* Equal Contribution)
International Conference on Machine Learning (ICML) 2024
Robustify Transformers with Robust Kernel Density Estimation [arXiv]
Xing Han, Tongzheng Ren, Tan Minh Nguyen, Khai Nguyen, Joydeep Ghosh, Nhat Ho
Advances in Neural Information Processing Systems (NeurIPS) 2023
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding [arXiv]
Tongzheng Ren*, Zhaolin Ren*, Na Li, Bo Dai (* Equal Contribution)
IEEE Conference on Decision and Control (CDC) 2023
Latent Variable Representation for Reinforcement Learning [arXiv] [ICLR version]
Tongzheng Ren*, Chenjun Xiao*, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai (* Equal Contribution)
International Conference on Learning Representation (ICLR) 2023
Hierarchical sliced Wasserstein distance [arXiv] [ICLR version]
Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Nguyen, Nhat Ho
International Conference on Learning Representation (ICLR) 2023
Spectral Decomposition Representation for Reinforcement Learning [arXiv] [ICLR version]
Tongzheng Ren*, Tianjun Zhang*, Lisa Lee, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai (* Equal Contribution)
International Conference on Learning Representation (ICLR) 2023
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning [arXiv] [UAI version]
Tongzheng Ren*, Tianjun Zhang*, Csaba Szepesvári, Bo Dai (* Equal Contribution)
Conference on Uncertainty in Artifical Intelligence (UAI) 2022
Making Linear MDP Practical via Contrastive Representation Learning [arXiv] [ICML version]
Tianjun Zhang*, Tongzheng Ren*, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai (* Equal Contribution)
International Conference on Machine Learning (ICML) 2022
Linear Bandit Algorithms with Sublinear Time Complexity [arXiv] [ICML version]
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] [AISTATS version]
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 [arXiv] [AAAI version]
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] [NeurIPS version]
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] [NeurIPS version]
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] [ACL version]
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: Lightweight Adversarial Training with Data Augmentation Improves Neural Network Training [arXiv] [CVPR version]
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
Preprints
Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models [arXiv]
Qiujiang Jin, Tongzheng Ren, Nhat Ho, Aryan Mokhtari
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering [arXiv]
Xing Han, Tongzheng Ren, Jing Hu, Joydeep Ghosh, Nhat Ho
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures [arXiv]
Tongzheng Ren*, Fuheng Cui*, Sujay Sanghavi, Nhat Ho (* Equal Contribution)
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models [arXiv]
Nhat Ho, Tongzheng Ren, Sujay Sanghavi, Purnamrita Sarkar, Rachel Ward (alpha-beta order)
Combinatorial Bandits without Total Order for Arms. [arXiv]
Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon, Sujay Sanghavi
Professional Activities
Conference Review: ICML, NeurIPS (Area Chair), ICLR, AAAI, AISTATS
Journal Review: JMLR, TMLR
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