Tight Complexity Bounds for Optimizing Composite Objectives

25 May 2016

Papers citing "Tight Complexity Bounds for Optimizing Composite Objectives"

34 / 34 papers shown

Title
Memory-Query Tradeoffs for Randomized Convex Optimization X. Chen Binghui Peng 36 6 0 21 Jun 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis Dachao Lin Yuze Han Haishan Ye Zhihua Zhang 19 11 0 15 Apr 2023
Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments Quoc Tran-Dinh 35 7 0 30 Mar 2023
Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing Kunal Jain J. PrabuchandranK. Tejas Bodas 14 2 0 21 Mar 2023
Stochastic Steffensen method Minda Zhao Zehua Lai Lek-Heng Lim ODL 15 3 0 28 Nov 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 50 11 0 17 Jun 2022
Efficient Convex Optimization Requires Superlinear Memory A. Marsden Vatsal Sharan Aaron Sidford Gregory Valiant 26 14 0 29 Mar 2022
Distributionally Robust Optimization via Ball Oracle Acceleration Y. Carmon Danielle Hausler 18 11 0 24 Mar 2022
Stochastic Primal-Dual Deep Unrolling Junqi Tang Subhadip Mukherjee Carola-Bibiane Schönlieb 22 4 0 19 Oct 2021
Stochastic Bias-Reduced Gradient Methods Hilal Asi Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 18 29 0 17 Jun 2021
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization Siqi Zhang Junchi Yang Cristóbal Guzmán Negar Kiyavash Niao He 33 61 0 29 Mar 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 36 14 0 21 Mar 2021
Machine Unlearning via Algorithmic Stability Enayat Ullah Tung Mai Anup B. Rao Ryan Rossi R. Arora 27 101 0 25 Feb 2021
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques Filip Hanzely Boxin Zhao Mladen Kolar FedML 24 52 0 19 Feb 2021
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik FedML 38 186 0 05 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 32 0 0 26 Aug 2020
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization Zhize Li Hongyan Bao Xiangliang Zhang Peter Richtárik ODL 26 125 0 25 Aug 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi-An Ma 50 23 0 18 Jun 2020
Minibatch vs Local SGD for Heterogeneous Distributed Learning Blake E. Woodworth Kumar Kshitij Patel Nathan Srebro FedML 22 198 0 08 Jun 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems Filip Hanzely D. Kovalev Peter Richtárik 35 17 0 11 Feb 2020
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 25 30 0 22 Oct 2019
Semi-Cyclic Stochastic Gradient Descent Hubert Eichner Tomer Koren H. B. McMahan Nathan Srebro Kunal Talwar 22 106 0 23 Apr 2019
Lower Bounds for Parallel and Randomized Convex Optimization Jelena Diakonikolas Cristóbal Guzmán 30 44 0 05 Nov 2018
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization Eric Balkanski Yaron Singer 14 31 0 12 Aug 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 33 569 0 04 Jul 2018
Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou Pan Xu Quanquan Gu 25 146 0 20 Jun 2018
Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law Max Simchowitz A. Alaoui Benjamin Recht 25 38 0 04 Apr 2018
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates Arnulf Jentzen Philippe von Wurstemberger 73 31 0 22 Mar 2018
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization Tomoya Murata Taiji Suzuki OffRL 27 28 0 01 Mar 2017
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 22 1,876 0 08 Oct 2016
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method Lihua Lei Michael I. Jordan 18 95 0 12 Sep 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 15 575 0 18 Mar 2016
An optimal randomized incremental gradient method Guanghui Lan Yi Zhou 23 220 0 08 Jul 2015
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization Yuchen Zhang Xiao Lin 35 261 0 10 Sep 2014