4 citations · 6 across the 6 of their papers we have counts for
6 papers
Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?
Jianhao Ma, Salar Fattahi
We explore the local landscape of low-rank matrix recovery, focusing on reconstructing a matrix with rank from linear measurements, some potential…
Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition
Jianhao Ma, Lingjun Guo, Salar Fattahi
This work analyzes the solution trajectory of gradient-based algorithms via a novel basis function decomposition. We show that, although solution trajectories of gradient-based alg…
Blessing of Nonconvexity in Deep Linear Models: Depth Flattens the Optimization Landscape Around the True Solution
Jianhao Ma, Salar Fattahi
This work characterizes the effect of depth on the optimization landscape of linear regression, showing that, despite their nonconvexity, deeper models have more desirable optimiza…
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization
Jianhao Ma, Salar Fattahi
In this work, we study the performance of sub-gradient method (SubGM) on a natural nonconvex and nonsmooth formulation of low-rank matrix recovery with -loss, where the goa…
Towards Understanding Generalization via Decomposing Excess Risk Dynamics
Jiaye Teng, Jianhao Ma, Yang Yuan
Generalization is one of the fundamental issues in machine learning. However, traditional techniques like uniform convergence may be unable to explain generalization under overpara…
Sign-RIP: A Robust Restricted Isometry Property for Low-rank Matrix Recovery
Jianhao Ma, Salar Fattahi
Restricted isometry property (RIP), essentially stating that the linear measurements are approximately norm-preserving, plays a crucial role in studying low-rank matrix recovery pr…