The Landscape of Empirical Risk for Non-convex Losses

22 July 2016

Papers citing "The Landscape of Empirical Risk for Non-convex Losses"

11 / 11 papers shown

Title
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 87 0 0 11 Jun 2024
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks Eshaan Nichani Alexandru Damian Jason D. Lee MLT 92 15 0 11 May 2023
Learning time-scales in two-layers neural networks Raphael Berthier Andrea Montanari Kangjie Zhou 83 33 0 28 Feb 2023
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias Lu Yu Krishnakumar Balasubramanian S. Volgushev Murat A. Erdogdu 65 50 0 14 Jun 2020
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach Ryo Karakida S. Akaho S. Amari FedML 106 142 0 04 Jun 2018
An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis Yuandong Tian MLT 95 216 0 02 Mar 2017
High-dimensional estimation with geometric constraints Y. Plan Roman Vershynin E. Yudovina 104 144 0 14 Apr 2014
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh Martin J. Wainwright 162 516 0 10 May 2013
Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach Y. Plan Roman Vershynin 143 457 0 06 Feb 2012
A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers S. Negahban Pradeep Ravikumar Martin J. Wainwright Bin Yu 241 1,377 0 13 Oct 2010
Matrix Completion from a Few Entries Raghunandan H. Keshavan Andrea Montanari Sewoong Oh 212 1,242 0 20 Jan 2009