Regularization and the small-ball method II: complexity dependent error rates

Journal of machine learning research (JMLR), 2016

27 August 2016

Papers citing "Regularization and the small-ball method II: complexity dependent error rates"

21 / 21 papers shown

Title
Complexity Dependent Error Rates for Physics-informed Statistical Learning via the Small-ball Method Diego Marcondes 108 0 0 27 Oct 2025
Optimal kernel regression bounds under energy-bounded noise Amon Lahr Johannes Köhler Anna Scampicchio Melanie Zeilinger 199 1 0 28 May 2025
Early-Stopped Mirror Descent for Linear Regression over Convex Bodies Tobias Wegel Gil Kur Patrick Rebeschini 218 0 0 05 Mar 2025
On the Importance of Gradient Norm in PAC-Bayesian BoundsNeural Information Processing Systems (NeurIPS), 2022 Itai Gat Yossi Adi Alex Schwing Tamir Hazan BDL 200 6 0 12 Oct 2022
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized ExperimentsInternational Conference on Machine Learning (ICML), 2022 Jinkun Lin Anqi Zhang Mathias Lécuyer Jinyang Li Aurojit Panda S. Sen TDI FedML 178 63 0 20 Jun 2022
Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive BiasInternational Conference on Machine Learning (ICML), 2022 Konstantin Donhauser Nicolò Ruggeri Stefan Stojanovic Fanny Yang 269 25 0 07 Mar 2022
On the robustness of minimum norm interpolators and regularized empirical risk minimizersAnnals of Statistics (Ann. Stat.), 2020 Geoffrey Chinot Matthias Löffler Sara van de Geer 328 22 0 01 Dec 2020
Minimax Estimation of Conditional Moment ModelsNeural Information Processing Systems (NeurIPS), 2020 Nishanth Dikkala Greg Lewis Lester W. Mackey Vasilis Syrgkanis 479 109 0 12 Jun 2020
Generic Error Bounds for the Generalized Lasso with Sub-Exponential DataSampling Theory, Signal Processing, and Data Analysis (TSPDA), 2020 Martin Genzel Christian Kipp 294 10 0 11 Apr 2020
Sum-of-squares meets square loss: Fast rates for agnostic tensor completionAnnual Conference Computational Learning Theory (COLT), 2019 Dylan J. Foster Andrej Risteski 114 4 0 30 May 2019
Robust high dimensional learning for Lipschitz and convex losses Geoffrey Chinot Guillaume Lecué M. Lerasle 353 18 0 10 May 2019
Robust learning and complexity dependent bounds for regularized problems Geoffrey Chinot 178 2 0 06 Feb 2019
Learning with Non-Convex Truncated Losses by SGD Yi Tian Xu Shenghuo Zhu Sen Yang Chi Zhang Rong Jin Tianbao Yang 142 37 0 21 May 2018
Structured Recovery with Heavy-tailed Measurements: A Thresholding Procedure and Optimal Rates Xiaohan Wei 193 11 0 16 Apr 2018
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization Martin Genzel A. Stollenwerk 144 7 0 13 Apr 2018
Robust machine learning by median-of-means : theory and practice Guillaume Lecué M. Lerasle OOD 398 166 0 28 Nov 2017
Solving Equations of Random Convex Functions via Anchored RegressionFoundations of Computational Mathematics (FoCM), 2017 S. Bahmani Justin Romberg 238 10 0 17 Feb 2017
Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functionsAnnals of Statistics (Ann. Stat.), 2017 Pierre Alquier V. Cottet Guillaume Lecué 381 61 0 05 Feb 2017
Learning from MOM's principles: Le Cam's approachStochastic Processes and their Applications (SPA), 2017 Lecué Guillaume Lerasle Matthieu 226 55 0 08 Jan 2017
A Convex Program for Mixed Linear Regression with a Recovery Guarantee for Well-Separated DataNeural Information Processing Systems (NeurIPS), 2016 Paul Hand Babhru Joshi 163 13 0 19 Dec 2016
On optimality of empirical risk minimization in linear aggregation Adrien Saumard 250 21 0 11 May 2016