107 citations · 165 across the 8 of their papers we have counts for
9 papers
Improving Computational Complexity in Statistical Models with Second-Order Information
Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi +1
It is known that when the statistical models are singular, i.e., the Fisher information matrix at the true parameter is degenerate, the fixed step-size gradient descent algorithm t…
On the computational and statistical complexity of over-parameterized matrix sensing
Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho +1
We consider solving the low rank matrix sensing problem with Factorized Gradient Descend (FGD) method when the true rank is unknown and over-specified, which we refer to as over-pa…
Predicting What You Already Know Helps: Provable Self-Supervised Learning
Jason D. Lee, Qi Lei, Nikunj Saunshi +1
Self-supervised representation learning solves auxiliary prediction tasks (known as pretext tasks) without requiring labeled data to learn useful semantic representations. These pr…
Robust Structured Statistical Estimation via Conditional Gradient Type Methods
Jiacheng Zhuo, Liu Liu, Constantine Caramanis
Structured statistical estimation problems are often solved by Conditional Gradient (CG) type methods to avoid the computationally expensive projection operation. However, the exis…
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization
Fanhua Shang, Bingkun Wei, Hongying Liu +2
Large-scale non-convex sparsity-constrained problems have recently gained extensive attention. Most existing deterministic optimization methods (e.g., GraSP) are not suitable for l…
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Jiacheng Zhuo, Qi Lei, Alexandros G. Dimakis +1
Large-scale machine learning training suffers from two prior challenges, specifically for nuclear-norm constrained problems with distributed systems: the synchronization slowdown d…