Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation

9 January 2023

Papers citing "Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation"

40 / 40 papers shown

Title
Stability and Risk Bounds of Iterative Hard Thresholding Xiao-Tong Yuan P. Li 49 12 0 17 Mar 2022
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization Qi Deng Wenzhi Gao 47 14 0 06 Jun 2021
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning Blake E. Woodworth Nathan Srebro 42 22 0 04 Jun 2021
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums Kaiwen Zhou Lai Tian Anthony Man-Cho So James Cheng 53 10 0 25 May 2021
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$ Yegor Klochkov Nikita Zhivotovskiy 58 62 0 22 Mar 2021
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization Karan N. Chadha Gary Cheng John C. Duchi 74 16 0 07 Jan 2021
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent Yunwen Lei Yiming Ying MLT 65 127 0 15 Jun 2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar MLT 47 194 0 12 Jun 2020
Sharper bounds for uniformly stable algorithms Olivier Bousquet Yegor Klochkov Nikita Zhivotovskiy 48 120 0 17 Oct 2019
On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond Xiao-Tong Yuan Ping Li 112 32 0 06 Aug 2019
A Generic Acceleration Framework for Stochastic Composite Optimization A. Kulunchakov Julien Mairal 53 43 0 03 Jun 2019
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes Yang You Jing Li Sashank J. Reddi Jonathan Hseu Sanjiv Kumar Srinadh Bhojanapalli Xiaodan Song J. Demmel Kurt Keutzer Cho-Jui Hsieh ODL 214 993 0 01 Apr 2019
The importance of better models in stochastic optimization Hilal Asi John C. Duchi 38 73 0 20 Mar 2019
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate Vitaly Feldman J. Vondrák 58 154 0 27 Feb 2019
Generalization Bounds for Uniformly Stable Algorithms Vitaly Feldman J. Vondrák 49 89 0 24 Dec 2018
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity Hilal Asi John C. Duchi 120 124 0 12 Oct 2018
Stochastic model-based minimization of weakly convex functions Damek Davis Dmitriy Drusvyatskiy 72 376 0 17 Mar 2018
Stochastic Methods for Composite and Weakly Convex Optimization Problems John C. Duchi Feng Ruan 32 127 0 24 Mar 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox Jialei Wang Weiran Wang Nathan Srebro 82 54 0 21 Feb 2017
Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$ - and $O(1/n^2)$ -type of Risk Bounds Lijun Zhang Tianbao Yang Rong Jin 40 48 0 07 Feb 2017
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 254 1,216 0 16 Aug 2016
The Landscape of Empirical Risk for Non-convex Losses Song Mei Yu Bai Andrea Montanari 96 312 0 22 Jul 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 221 3,205 0 15 Jun 2016
Efficient Distributed Learning with Sparsity Jialei Wang Mladen Kolar Nathan Srebro Tong Zhang FedML 59 152 0 25 May 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 96 580 0 18 Mar 2016
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression Aymeric Dieuleveut Nicolas Flammarion Francis R. Bach ODL 51 226 0 17 Feb 2016
Train faster, generalize better: Stability of stochastic gradient descent Moritz Hardt Benjamin Recht Y. Singer 111 1,238 0 03 Sep 2015
Towards stability and optimality in stochastic gradient descent Panos Toulis Dustin Tran E. Airoldi 67 56 0 10 May 2015
Competing with the Empirical Risk Minimizer in a Single Pass Roy Frostig Rong Ge Sham Kakade Aaron Sidford 69 100 0 20 Dec 2014
Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi Virginia Smith Martin Takáč Jonathan Terhorst S. Krishnan Thomas Hofmann Michael I. Jordan 82 353 0 04 Sep 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 131 1,823 0 01 Jul 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 150 738 0 19 Mar 2014
Communication Efficient Distributed Optimization using an Approximate Newton-type Method Ohad Shamir Nathan Srebro Tong Zhang 86 556 0 30 Dec 2013
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin Ohad Shamir Karthik Sridharan 151 767 0 26 Sep 2011
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt Nicolas Le Roux Francis R. Bach 185 583 0 12 Sep 2011
A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers S. Negahban Pradeep Ravikumar Martin J. Wainwright Bin Yu 416 1,378 0 13 Oct 2010
Optimistic Rates for Learning with a Smooth Loss Nathan Srebro Karthik Sridharan Ambuj Tewari 153 282 0 20 Sep 2010
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization Alekh Agarwal Peter L. Bartlett Pradeep Ravikumar Martin J. Wainwright 173 250 0 03 Sep 2010
High-dimensional generalized linear models and the lasso Sara van de Geer 563 755 0 04 Apr 2008
Sparse Additive Models Pradeep Ravikumar John D. Lafferty Han Liu Larry A. Wasserman 427 574 0 28 Nov 2007