72 citations · 542 across the 65 of their papers we have counts for
18 papers · 1 filter
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
T. Mitchell Roddenberry, Santiago Segarra, Anastasios Kyrillidis
We study the role of the constraint set in determining the solution to low-rank, positive semidefinite (PSD) matrix sensing problems. The setting we consider involves rank-one sens…
On Generalization of Adaptive Methods for Over-parameterized Linear Regression
Vatsal Shah, Soumya Basu, Anastasios Kyrillidis +1
Over-parameterization and adaptive methods have played a crucial role in the success of deep learning in the last decade. The widespread use of over-parameterization has forced us…
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data…
Learning Sparse Distributions using Iterative Hard Thresholding
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such…
Minimum weight norm models do not always generalize well for over-parameterized problems
Vatsal Shah, Anastasios Kyrillidis, Sujay Sanghavi
This work is substituted by the paper in arXiv:2011.14066. Stochastic gradient descent is the de facto algorithm for training deep neural networks (DNNs). Despite its popularity, i…
Implicit regularization and solution uniqueness in over-parameterized matrix sensing
Kelly Geyer, Anastasios Kyrillidis, Amir Kalev
We consider whether algorithmic choices in over-parameterized linear matrix factorization introduce implicit regularization. We focus on noiseless matrix sensing over rank- posi…