8 citations · 23 across the 28 of their papers we have counts for
17 papers · 1 filter
Risk-Averse Wasserstein Distributionally Robust Online Learning
Guixian Chen, Salar Fattahi, Soroosh Shafiee
We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a Wasserstein ambi…
Understanding the Implicit Regularization of Gradient Descent in Over-parameterized Models
Jianhao Ma, Geyu Liang, Salar Fattahi
Implicit regularization refers to the tendency of local search algorithms to converge to low-dimensional solutions, even when such structures are not explicitly enforced. Despite i…
RANSAC Revisited: An Improved Algorithm for Robust Subspace Recovery under Adversarial and Noisy Corruptions
Guixian Chen, Jianhao Ma, Salar Fattahi
In this paper, we study the problem of robust subspace recovery (RSR) in the presence of both strong adversarial corruptions and Gaussian noise. Specifically, given a limited numbe…
Enhancing Performance of Explainable AI Models with Constrained Concept Refinement
Geyu Liang, Senne Michielssen, Salar Fattahi
The trade-off between accuracy and interpretability has long been a challenge in machine learning (ML). This tension is particularly significant for emerging interpretable-by-desig…
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
Naichen Shi, Salar Fattahi, Raed Al Kontar
In this work, we study the problem of common and unique feature extraction from noisy data. When we have N observation matrices from N different and associated sources corrupted by…
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
Jianhao Ma, Salar Fattahi
We study the problem of symmetric matrix completion, where the goal is to reconstruct a positive semidefinite matrix of rank-, paramete…