Nonlinear Meta-Learning Can Guarantee Faster Rates

20 July 2023

Papers citing "Nonlinear Meta-Learning Can Guarantee Faster Rates"

39 / 39 papers shown

Title
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples Thomas T. Zhang Bruce D. Lee Ingvar M. Ziemann George J. Pappas Nikolai Matni CML OOD 105 0 0 15 Oct 2024
Learning with Shared Representations: Statistical Rates and Efficient Algorithms Xiaochun Niu Lili Su Jiaming Xu Pengkun Yang FedML 51 2 0 07 Sep 2024
Transformers are Minimax Optimal Nonparametric In-Context Learners Juno Kim Tai Nakamaki Taiji Suzuki 79 13 0 22 Aug 2024
Metalearning with Very Few Samples Per Task Maryam Aliakbarpour Konstantina Bairaktari Gavin Brown Adam D. Smith Nathan Srebro Jonathan Ullman VLM 86 3 0 21 Dec 2023
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 99 8 0 12 Dec 2023
Meta-Learning Operators to Optimality from Multi-Task Non-IID Data Thomas T. Zhang Leonardo F. Toso James Anderson Nikolai Matni 94 13 0 08 Aug 2023
Optimally tackling covariate shift in RKHS-based nonparametric regression Cong Ma Reese Pathak Martin J. Wainwright 33 44 0 06 May 2022
An elementary analysis of ridge regression with random design Jaouad Mourtada Lorenzo Rosasco 44 11 0 16 Mar 2022
Meta-strategy for Learning Tuning Parameters with Guarantees Dimitri Meunier Pierre Alquier 69 8 0 04 Feb 2021
A Distribution-Dependent Analysis of Meta-Learning Mikhail Konobeev Ilja Kuzborskij Csaba Szepesvári OOD 56 5 0 31 Oct 2020
On the Theory of Transfer Learning: The Importance of Task Diversity Nilesh Tripuraneni Michael I. Jordan Chi Jin 100 219 0 20 Jun 2020
Learning Polynomials of Few Relevant Dimensions Sitan Chen Raghu Meka 34 39 0 28 Apr 2020
Provable Meta-Learning of Linear Representations Nilesh Tripuraneni Chi Jin Michael I. Jordan OOD 96 191 0 26 Feb 2020
Few-Shot Learning via Learning the Representation, Provably S. Du Wei Hu Sham Kakade Jason D. Lee Qi Lei SSL 46 260 0 21 Feb 2020
Meta-learning for mixed linear regression Weihao Kong Raghav Somani Zhao Song Sham Kakade Sewoong Oh 56 66 0 20 Feb 2020
Adaptive Gradient-Based Meta-Learning Methods M. Khodak Maria-Florina Balcan Ameet Talwalkar FedML 77 355 0 06 Jun 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 45 243 0 27 Apr 2019
Eigenvalue and Generalized Eigenvalue Problems: Tutorial Benyamin Ghojogh Fakhri Karray Mark Crowley 53 124 0 25 Mar 2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization Giulia Denevi C. Ciliberto Riccardo Grazzi Massimiliano Pontil 67 110 0 25 Mar 2019
Online Meta-Learning Chelsea Finn Aravind Rajeswaran Sham Kakade Sergey Levine CLL 63 253 0 22 Feb 2019
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences Motonobu Kanagawa Philipp Hennig Dino Sejdinovic Bharath K. Sriperumbudur GP BDL 126 342 0 06 Jul 2018
Domain Generalization by Marginal Transfer Learning Gilles Blanchard A. Deshmukh Ürün Dogan Gyemin Lee Clayton Scott OOD 82 283 0 21 Nov 2017
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings Motonobu Kanagawa Bharath K. Sriperumbudur Kenji Fukumizu 75 45 0 01 Sep 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 811 11,894 0 09 Mar 2017
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm Simon Fischer Ingo Steinwart 170 151 0 23 Feb 2017
Optimal Rates For Regularization Of Statistical Inverse Learning Problems Gilles Blanchard Nicole Mücke 439 143 0 14 Apr 2016
Generalization Properties of Learning with Random Features Alessandro Rudi Lorenzo Rosasco MLT 68 331 0 14 Feb 2016
Less is More: Nyström Computational Regularization Alessandro Rudi Raffaello Camoriano Lorenzo Rosasco 43 277 0 16 Jul 2015
The Benefit of Multitask Representation Learning Andreas Maurer Massimiliano Pontil Bernardino Romera-Paredes SSL 102 375 0 23 May 2015
A chain rule for the expected suprema of Gaussian processes Andreas Maurer 51 24 0 10 Nov 2014
A useful variant of the Davis--Kahan theorem for statisticians Yi Yu Tengyao Wang R. Samworth 92 577 0 04 May 2014
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates Yuchen Zhang John C. Duchi Martin J. Wainwright 320 378 0 22 May 2013
Principal support vector machines for linear and nonlinear sufficient dimension reduction Bing Li A. Artemiou Lexin Li 68 105 0 13 Mar 2012
Spectral clustering and the high-dimensional stochastic blockmodel Karl Rohe S. Chatterjee Bin Yu 252 934 0 09 Jul 2010
Universality, Characteristic Kernels and RKHS Embedding of Measures Bharath K. Sriperumbudur Kenji Fukumizu Gert R. G. Lanckriet 213 529 0 03 Mar 2010
Kernel dimension reduction in regression Kenji Fukumizu Francis R. Bach Michael I. Jordan 211 313 0 13 Aug 2009
Dimension reduction for nonelliptically distributed predictors Bing Li Yuexiao Dong 76 114 0 24 Apr 2009
Domain Adaptation: Learning Bounds and Algorithms Yishay Mansour M. Mohri Afshin Rostamizadeh 288 799 0 19 Feb 2009
A Tutorial on Spectral Clustering U. V. Luxburg 277 10,532 0 01 Nov 2007