Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

12 June 2020

Papers citing "Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses"

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Title
Leave-One-Out Stable Conformal Prediction Kiljae Lee Yuan Zhang 36 0 0 16 Apr 2025
Understanding the Generalization Error of Markov algorithms through Poissonization Benjamin Dupuis Maxime Haddouche George Deligiannidis Umut Simsekli 47 0 0 11 Feb 2025
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization Shun Takagi Li Xiong Fumiyuki Kato Yang Cao Masatoshi Yoshikawa 3DH 46 2 0 13 May 2024
Uniformly Stable Algorithms for Adversarial Training and Beyond Jiancong Xiao Jiawei Zhang Zhimin Luo Asuman Ozdaglar AAML 48 0 0 03 May 2024
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization Roi Livni MLT 37 1 0 07 Apr 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 47 18 0 09 Jan 2024
Convex SGD: Generalization Without Early Stopping Julien Hendrickx A. Olshevsky MLT LRM 25 1 0 08 Jan 2024
Generalization Bounds for Label Noise Stochastic Gradient Descent Jung Eun Huh Patrick Rebeschini 13 1 0 01 Nov 2023
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization Liang Zhang Junchi Yang Amin Karbasi Niao He 28 2 0 26 Oct 2023
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms Minghao Yang Xiyuan Wei Tianbao Yang Yiming Ying 37 1 0 07 Jul 2023
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent Lingjiong Zhu Mert Gurbuzbalaban Anant Raj Umut Simsekli 31 6 0 20 May 2023
Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Renyi's Entropy Perspective Yuxin Dong Tieliang Gong H. Chen Chen Li 23 4 0 02 May 2023
Learning Trajectories are Generalization Indicators Jingwen Fu Zhizheng Zhang Dacheng Yin Yan Lu Nanning Zheng AI4CE 30 3 0 25 Apr 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 94 167 0 01 Mar 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Arun Ganesh Daogao Liu Sewoong Oh Abhradeep Thakurta ODL 27 12 0 20 Feb 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 34 2 0 09 Jan 2023
ReSQueing Parallel and Private Stochastic Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Y. Lee Daogao Liu Aaron Sidford Kevin Tian FedML 22 12 0 01 Jan 2023
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 52 7 0 24 Nov 2022
Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States Ziqiao Wang Yongyi Mao 27 10 0 19 Nov 2022
Private optimization in the interpolation regime: faster rates and hardness results Hilal Asi Karan N. Chadha Gary Cheng John C. Duchi 47 5 0 31 Oct 2022
Differentially Private Online-to-Batch for Smooth Losses Qinzi Zhang Hoang Tran Ashok Cutkosky FedML 46 4 0 12 Oct 2022
Stability Analysis and Generalization Bounds of Adversarial Training Jiancong Xiao Yanbo Fan Ruoyu Sun Jue Wang Zhimin Luo AAML 29 30 0 03 Oct 2022
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Alireza Mousavi-Hosseini Sejun Park M. Girotti Ioannis Mitliagkas Murat A. Erdogdu MLT 324 48 0 29 Sep 2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods Puyu Wang Yunwen Lei Yiming Ying Ding-Xuan Zhou 16 18 0 16 Sep 2022
Differentially Private Stochastic Gradient Descent with Low-Noise Puyu Wang Yunwen Lei Yiming Ying Ding-Xuan Zhou FedML 43 5 0 09 Sep 2022
Uniform Stability for First-Order Empirical Risk Minimization Amit Attia Tomer Koren 20 5 0 17 Jul 2022
When Does Differentially Private Learning Not Suffer in High Dimensions? Xuechen Li Daogao Liu Tatsunori Hashimoto Huseyin A. Inan Janardhan Kulkarni Y. Lee Abhradeep Thakurta 32 58 0 01 Jul 2022
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization Yi Ma T. V. Marinov Tong Zhang 17 8 0 03 Jun 2022
Selective Classification Via Neural Network Training Dynamics Stephan Rabanser Anvith Thudi Kimia Hamidieh Adam Dziedzic Nicolas Papernot 29 21 0 26 May 2022
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD Konstantinos E. Nikolakakis Farzin Haddadpour Amin Karbasi Dionysios S. Kalogerias 40 17 0 26 Apr 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 29 3 0 22 Apr 2022
Making Progress Based on False Discoveries Roi Livni 38 0 0 19 Apr 2022
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond Matan Schliserman Tomer Koren 24 23 0 27 Feb 2022
Benign Underfitting of Stochastic Gradient Descent Tomer Koren Roi Livni Yishay Mansour Uri Sherman MLT 20 13 0 27 Feb 2022
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization I Zaghloul Amir Roi Livni Nathan Srebro 27 6 0 27 Feb 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 36 100 0 28 Jan 2022
Differentially Private SGDA for Minimax Problems Zhenhuan Yang Shu Hu Yunwen Lei Kush R. Varshney Siwei Lyu Yiming Ying 36 19 0 22 Jan 2022
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape Devansh Bisla Jing Wang A. Choromańska 25 34 0 20 Jan 2022
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics A. Banerjee Tiancong Chen Xinyan Li Yingxue Zhou 34 8 0 09 Jan 2022
Stable Conformal Prediction Sets Eugène Ndiaye 35 20 0 19 Dec 2021
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith M. Suriyakumar Om Thakkar Abhradeep Thakurta 13 49 0 01 Dec 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 30 14 0 22 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 38 6 0 14 Oct 2021
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications Ziqiao Wang Yongyi Mao FedML MLT 37 22 0 07 Oct 2021
Stochastic Training is Not Necessary for Generalization Jonas Geiping Micah Goldblum Phillip E. Pope Michael Moeller Tom Goldstein 89 72 0 29 Sep 2021
Stability and Generalization for Randomized Coordinate Descent Puyu Wang Liang Wu Yunwen Lei 27 7 0 17 Aug 2021
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints Shaojie Li Yong Liu 20 13 0 19 Jul 2021
The Power of Sampling: Dimension-free Risk Bounds in Private ERM Yin Tat Lee Daogao Liu Zhou Lu 16 3 0 28 May 2021
Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time Raef Bassily Cristóbal Guzmán Anupama Nandi 54 66 0 01 Mar 2021
Stability of SGD: Tightness Analysis and Improved Bounds Yikai Zhang Wenjia Zhang Sammy Bald Vamsi Pingali Chao Chen Mayank Goswami MLT 19 36 0 10 Feb 2021