On Nonconvex Optimization for Machine Learning: Gradients,
Stochasticity, and Saddle Points

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

13 February 2019

Praneeth Netrapalli

Michael I. Jordan

Papers citing "On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points"

11 / 11 papers shown

Title
Mnemosyne: Learning to Train Transformers with Transformers Deepali Jain K. Choromanski Kumar Avinava Dubey Sumeet Singh Vikas Sindhwani Tingnan Zhang Jie Tan OffRL 39 9 0 02 Feb 2023
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data Harsh Rangwani Sumukh K Aithal Mayank Mishra R. Venkatesh Babu 31 29 0 28 Dec 2022
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization Jun-Kun Wang Jacob D. Abernethy 11 7 0 04 Oct 2020
Distributed Gradient Flow: Nonsmoothness, Nonconvexity, and Saddle Point Evasion Brian Swenson Ryan W. Murray H. Vincent Poor S. Kar 12 16 0 12 Aug 2020
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions V. Patel 21 23 0 01 Apr 2020
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval Yan Shuo Tan Roman Vershynin 22 35 0 28 Oct 2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai J. Lee 24 116 0 03 Oct 2019
Quantum algorithm for finding the negative curvature direction in non-convex optimization Kaining Zhang Min-hsiu Hsieh Liu Liu Dacheng Tao 18 3 0 17 Sep 2019
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization Stefan Vlaski Ali H. Sayed ODL 29 21 0 19 Aug 2019
Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points Stefan Vlaski Ali H. Sayed 27 53 0 03 Jul 2019
Multichannel Sparse Blind Deconvolution on the Sphere Yanjun Li Y. Bresler 9 16 0 26 May 2018