Research
My research lies at the intersection of optimization and statistics. You can check my google scholar for a full list of my work.
Semidefinite programming
Revisiting Spectral Bundle Methods: Primal-dual (Sub)linear Convergence Rates [arxiv] [slides]
Ding and Grimmer
A Strict Complementarity Approach to Error Bound and Sensitivity of Solution of Conic Programs [arxiv]
Ding and Udell
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity [arxiv] [slides]
Ding, Yurtsever, Cevher, Tropp, and Udell
Winner of Student Paper Prize 2019 of INFORMS Optimization Society
On the simplicity and conditioning of low rank semidefinite programs [arxiv]
Ding and Udell
Higher-Order Cone Programming [arxiv] [slides]
Ding and Lim
Working Paper (2018)
Statistical nonconvex optimization
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence [arxiv]
Charisopoulos, Chen, Davis, Diaz, Ding, and Drusvyatskiy
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle [arxiv]
Ding, Zhang, and Chen
Submitted (2021)
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis [arxiv] [slides]
Ding and Chen
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery [arxiv]
Fan, Ding, Chen, and Udell
Overparametrization
Flat minima generalize for low-rank matrix recovery [arxiv] [slides]
Ding, Drusvyatskiy, Fazel, and Harchaoui
Submitted (2022)
Jiang, Chen, and Ding
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery [arxiv]
Ding, Jiang, Chen, Qu, and Zhu.
Frank-Wolfe
kFW: A Frank-Wolfe style algorithm with stronger subproblem oracles [arxiv] [slides]
Ding, Fan, and Udell
Submitted (2022)
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence [arxiv]
Ding, Fei, Xu, and Yang
Frank-Wolfe Style Algorithms for Large Scale Optimization [arxiv]
Ding and Udell