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09/21/2022
Instance-dependent uniform tail bounds for empirical processes
We formulate a uniform tail bound for empirical processes indexed by a c...
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10/28/2021
Decentralized Feature-Distributed Optimization for Generalized Linear Models
We consider the "all-for-one" decentralized learning problem for general...
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03/12/2021
Max-Linear Regression by Scalable and Guaranteed Convex Programming
We consider the multivariate max-linear regression problem where the mod...
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04/06/2020
Low-Rank Matrix Estimation From Rank-One Projections by Unlifted Convex Optimization
We study an estimator with a convex formulation for recovery of low-rank...
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04/05/2020
Nearly Optimal Robust Mean Estimation via Empirical Characteristic Function
We propose an estimator for the mean of random variables in separable re...
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10/25/2019
Phase Retrieval of Low-Rank Matrices by Anchored Regression
We study the low-rank phase retrieval problem, where we try to recover a...
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08/26/2019
Convex Programming for Estimation in Nonlinear Recurrent Models
We propose a formulation for nonlinear recurrent models that includes si...
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06/19/2018
Estimation from Non-Linear Observations via Convex Programming with Application to Bilinear Regression
We propose a computationally efficient estimator, formulated as a convex...
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02/17/2017
Solving Equations of Random Convex Functions via Anchored Regression
We consider the question of estimating a solution to a system of equatio...
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10/13/2016
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
We propose a flexible convex relaxation for the phase retrieval problem ...
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09/07/2012
Learning Model-Based Sparsity via Projected Gradient Descent
Several convex formulation methods have been proposed previously for sta...
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03/25/2012
Greedy Sparsity-Constrained Optimization
Sparsity-constrained optimization has wide applicability in machine lear...
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07/22/2011