Jianhao Ma
About me
I'm currently a fourth-year Ph.D. candidate at Umich. I am fortunate to be advised by Professor Salar Fattahi. My research focus on large-scale optimization and robust machine learning. I welcome future collaborations. Please feel free to contact me via Email!
Research Interests
General optimization and generalization in modern machine learning.
Algorithmic robust statistics.
Empirical process and its application.
Preprints
Robust Sparse Mean Estimation via Incremental Learning [arxiv]
Jianhao Ma, Rui Ray Chen, Yinghui He, Salar Fattahi, Wei Hu
Publications
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization [arxiv]
Journal of Machine Learning Research (JMLR) 2023
Jianhao Ma, Salar Fattahi
Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition [arxiv]
International Conference on Learning Representations (ICLR) 2023
Jianhao Ma, Lingjun Guo, Salar Fattahi
Blessing of Nonconvexity in Deep Linear Models: Depth Flattens the Optimization Landscape Around the True Solution [arxiv]
Advances in Neural Information Processing Systems (NeurIPS) 2022 (Spotlight)
Jianhao Ma, Salar Fattahi
Towards Understanding Generalization via Decomposing Excess Risk Dynamics [paper] [arxiv]
International Conference on Learning Representations (ICLR) 2022
Jiaye Teng*, Jianhao Ma*, Yang Yuan
Sign-RIP: A Robust Restricted Isometry Property for Low-rank Matrix Recovery [paper][arxiv]
Jianhao Ma, Salar Fattahi
NeurIPS Workshop on Optimization for Machine Learning, 2021
News
March 2024: I will attend INFORMS Optimization Society Conference.
March 2024: Our paper has been accepted at ICLR BGPT workshop.
March 2024: Thrilled to receive Rackham Predoctoral Fellowship.
February 2024: I will intern at FAIR Labs in Meta with Lin Xiao this summer.
February 2024: New paper about the global convergence of GD for unregularized matrix completion.
Services
Reviewer of IEEE TIT, IEEE TSP, ICML, NeurIPS, ICLR, AISTATS.
Quate
Always consider a problem under the minimum structure in which it makes sense. —— Gustave Choquet
|