Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes

25 May 2018

Papers citing "Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes"

24 / 24 papers shown

Title
The Optimization Landscape of SGD Across the Feature Learning Strength Alexander B. Atanasov Alexandru Meterez James B. Simon Cengiz Pehlevan 50 2 0 06 Oct 2024
How Feature Learning Can Improve Neural Scaling Laws Blake Bordelon Alexander B. Atanasov Cengiz Pehlevan 57 12 0 26 Sep 2024
Scaling Laws in Linear Regression: Compute, Parameters, and Data Licong Lin Jingfeng Wu Sham Kakade Peter L. Bartlett Jason D. Lee LRM 44 15 0 12 Jun 2024
Spectral Algorithms on Manifolds through Diffusion Weichun Xia Lei Shi 16 1 0 06 Mar 2024
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent Lingjiong Zhu Mert Gurbuzbalaban Anant Raj Umut Simsekli 34 6 0 20 May 2023
Optimality of Robust Online Learning Zheng-Chu Guo A. Christmann Lei Shi 29 9 0 20 Apr 2023
On the Optimality of Misspecified Spectral Algorithms Hao Zhang Yicheng Li Qian Lin 18 15 0 27 Mar 2023
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks Ilja Kuzborskij Csaba Szepesvári 21 4 0 28 Dec 2022
Online Regularized Learning Algorithm for Functional Data Yuan Mao Zheng-Chu Guo 21 4 0 24 Nov 2022
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 30 9 0 20 Nov 2022
Provable Generalization of Overparameterized Meta-learning Trained with SGD Yu Huang Yingbin Liang Longbo Huang MLT 28 8 0 18 Jun 2022
Active Labeling: Streaming Stochastic Gradients Vivien A. Cabannes Francis R. Bach Vianney Perchet Alessandro Rudi 61 2 0 26 May 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 38 7 0 15 May 2022
The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models Yiling Luo X. Huo Y. Mei 21 0 0 29 Apr 2022
On the Benefits of Large Learning Rates for Kernel Methods Gaspard Beugnot Julien Mairal Alessandro Rudi 27 11 0 28 Feb 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning A. Bietti Chen-Yu Wei Miroslav Dudík John Langford Zhiwei Steven Wu FedML 24 43 0 10 Feb 2022
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints Shaojie Li Yong Liu 26 13 0 19 Jul 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro Cédric Gerbelot Hugo Cui Sebastian Goldt Florent Krzakala M. Mézard Lenka Zdeborová 35 135 0 16 Feb 2021
Fast rates in structured prediction Vivien A. Cabannes Alessandro Rudi Francis R. Bach 20 19 0 01 Feb 2021
Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves Rahul Singh Liyuan Xu Arthur Gretton OffRL 63 27 0 10 Oct 2020
When Does Preconditioning Help or Hurt Generalization? S. Amari Jimmy Ba Roger C. Grosse Xuechen Li Atsushi Nitanda Taiji Suzuki Denny Wu Ji Xu 36 32 0 18 Jun 2020
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent Yunwen Lei Yiming Ying MLT 35 126 0 15 Jun 2020
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate Vitaly Feldman J. Vondrák 35 154 0 27 Feb 2019
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm Simon Fischer Ingo Steinwart 13 148 0 23 Feb 2017