228 citations · 726 across the 22 of their papers we have counts for
23 papers
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization
Cong Ma, Xingyu Xu, Tian Tong +1
Many problems encountered in science and engineering can be formulated as estimating a low-rank object (e.g., matrices and tensors) from incomplete, and possibly corrupted, linear…
High-probability sample complexities for policy evaluation with linear function approximation
Gen Li, Weichen Wu, Yuejie Chi +3
This paper is concerned with the problem of policy evaluation with linear function approximation in discounted infinite horizon Markov decision processes. We investigate the sample…
Conformalized matrix completion
Yu Gui, Rina Foygel Barber, Cong Ma
Matrix completion aims to estimate missing entries in a data matrix, using the assumption of a low-complexity structure (e.g., low rank) so that imputation is possible. While many…
Optimal tuning-free convex relaxation for noisy matrix completion
Yuepeng Yang, Cong Ma
This paper is concerned with noisy matrix completion--the problem of recovering a low-rank matrix from partial and noisy entries. Under uniform sampling and incoherence assumptions…
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
Harry Dong, Tian Tong, Cong Ma +1
An increasing number of data science and machine learning problems rely on computation with tensors, which better capture the multi-way relationships and interactions of data than…
Pessimism for Offline Linear Contextual Bandits using Confidence Sets
Gene Li, Cong Ma, Nathan Srebro
We present a family of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell…