I am a fifth-year Ph.D. student in Computer Science Department, Duke University. I am fortunate to be advised by Prof. Rong Ge. Before coming to Duke, I received B.S. in Statistics from Peking University in 2019.
In summer 2023, I worked with Prof. Tengyu Ma at Stanford. In summer 2022, I was an applied science intern at AWS AI. In summer 2018, I was an intern in Industrial and Systems Engineering (ISyE), Georgia Tech, working with Prof. Tuo Zhao.
My research interests are in optimization and theoretical machine learning. Recently, I am particularly interested in deep learning theory.
Publications and Preprints
* denotes equal contribution, (α-β order) denotes alphabetical ordering-
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
Mo Zhou, Rong Ge.
International Conference on Machine Learning (ICML), 2023. -
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
Xingyu Zhu*, Zixuan Wang*, Xiang Wang, Mo Zhou, Rong Ge.
International Conference on Learning Representations (ICLR), 2023 -
Depth-Separation with Multilayer Mean-Field Networks.
Yunwei Ren, Mo Zhou, Rong Ge.
International Conference on Learning Representations (ICLR), 2023. Notable-top-25%. -
Plateau in Monotonic Linear Interpolation–A “Biased” View of Loss Landscape for Deep Networks
Xiang Wang, Annie N Wang, Mo Zhou, Rong Ge.
International Conference on Learning Representations (ICLR), 2023 -
Understanding The Robustness of Self-supervised Learning Through Topic Modeling
Zeping Luo*, Shiyou Wu*, Cindy Weng*, Mo Zhou, Rong Ge
International Conference on Learning Representations (ICLR), 2023 -
Understanding Deflation Process in Over-parametrized Tensor Decomposition
(α-β order) Rong Ge*, Yunwei Ren*, Xiang Wang*, Mo Zhou*
Conference on Neural Information Processing Systems (NeurIPS), 2021. -
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
Mo Zhou, Rong Ge, Chi Jin
Conference on Learning Theory (COLT), 2021. -
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Tianyi Liu*, Minshuo Chen*, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
Conference on Neural Information Processing Systems (NeurIPS), 2019. -
Towards Understanding the Importance of Noise in Training Neural Networks
Mo Zhou*, Tianyi Liu*, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao
International Conference on Machine Learning (ICML), 2019. Long Talk.
Presentations
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A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
COLT 2021, Aug. 2021
Theory of Overparameterized Machine Learning (TOPML) 2021, Apr. 2021
Duke Deep Learning Reading Group, Apr. 2021
THEORINET Journal Club/MODL Reading Group, Feb. 2021
Teaching
- CPS590.04 Machine Learning Algorithms, 2021 Spring. TA
- CPS330 Design and Analysis of Algorithms, 2020 Fall. TA
- CPS330 Design and Analysis of Algorithms, 2020 Spring. TA
Services
- Reviewer for ICML, ICLR, NeurIPS, JMLR, Mathematical Programming, STOC.
Education
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Duke University, 2019 - present
Ph.D. in Computer Science -
Peking University, 2015 - 2019
B.S. in Statistics
