Learning without Concentration for General Loss Functions

13 October 2014

Papers citing "Learning without Concentration for General Loss Functions"

15 / 15 papers shown

Title
On the Concentration of the Minimizers of Empirical Risks Paul Escande 23 2 0 03 Apr 2023
Uniform Risk Bounds for Learning with Dependent Data Sequences Fabien Lauer 20 1 0 21 Mar 2023
Statistical Learning Theory for Control: A Finite Sample Perspective Anastasios Tsiamis Ingvar M. Ziemann Nikolai Matni George J. Pappas 23 73 0 12 Sep 2022
Outlier Robust and Sparse Estimation of Linear Regression Coefficients Takeyuki Sasai Hironori Fujisawa 27 4 0 24 Aug 2022
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition Varun Kanade Patrick Rebeschini Tomas Vaskevicius 19 10 0 23 Feb 2022
Kalman Filtering with Adversarial Corruptions Sitan Chen Frederic Koehler Ankur Moitra Morris Yau AAML 27 10 0 11 Nov 2021
Learning from MOM's principles: Le Cam's approach Lecué Guillaume Lerasle Matthieu 38 52 0 08 Jan 2017
On optimality of empirical risk minimization in linear aggregation Adrien Saumard 28 21 0 11 May 2016
Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes Peter Grünwald Nishant A. Mehta 37 71 0 01 May 2016
High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions Martin Genzel 24 45 0 10 Feb 2016
On the gap between RIP-properties and sparse recovery conditions S. Dirksen Guillaume Lecué Holger Rauhut 123 18 0 20 Apr 2015
`local' vs. `global' parameters -- breaking the gaussian complexity barrier S. Mendelson 32 24 0 09 Apr 2015
Statistical consistency and asymptotic normality for high-dimensional robust M-estimators Po-Ling Loh 37 192 0 01 Jan 2015
Performance of empirical risk minimization in linear aggregation Guillaume Lecué S. Mendelson FedML 51 40 0 24 Feb 2014
Learning without Concentration S. Mendelson 85 334 0 01 Jan 2014