Timezone: »
Poster
Concentration inequalities under sub-Gaussian and sub-exponential conditions
Andreas Maurer · Massimiliano Pontil
We prove analogues of the popular bounded difference inequality (also called McDiarmid's inequality) for functions of independent random variables under sub-gaussian and sub-exponential conditions. Applied to vector-valued concentration and the method of Rademacher complexities these inequalities allow an easy extension of uniform convergence results for PCA and linear regression to the case potentially unbounded input- and output variables.
Author Information
Andreas Maurer
Massimiliano Pontil (IIT & UCL)
More from the Same Authors
-
2021 : Linear Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport »
Vladimir Kostic · Saverio Salzo · Massimiliano Pontil -
2022 Poster: Conditional Meta-Learning of Linear Representations »
Giulia Denevi · Massimiliano Pontil · Carlo Ciliberto -
2022 Spotlight: Conditional Meta-Learning of Linear Representations »
Giulia Denevi · Massimiliano Pontil · Carlo Ciliberto -
2022 Spotlight: Lightning Talks 3B-1 »
Tianying Ji · Tongda Xu · Giulia Denevi · Aibek Alanov · Martin Wistuba · Wei Zhang · Yuesong Shen · Massimiliano Pontil · Vadim Titov · Yan Wang · Yu Luo · Daniel Cremers · Yanjun Han · Arlind Kadra · Dailan He · Josif Grabocka · Zhengyuan Zhou · Fuchun Sun · Carlo Ciliberto · Dmitry Vetrov · Mingxuan Jing · Chenjian Gao · Aaron Flores · Tsachy Weissman · Han Gao · Fengxiang He · Kunzan Liu · Wenbing Huang · Hongwei Qin -
2022 Spotlight: A gradient estimator via L1-randomization for online zero-order optimization with two point feedback »
Arya Akhavan · Evgenii Chzhen · Massimiliano Pontil · Alexandre Tsybakov -
2022 Poster: A gradient estimator via L1-randomization for online zero-order optimization with two point feedback »
Arya Akhavan · Evgenii Chzhen · Massimiliano Pontil · Alexandre Tsybakov -
2022 Poster: Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces »
Vladimir Kostic · Pietro Novelli · Andreas Maurer · Carlo Ciliberto · Lorenzo Rosasco · Massimiliano Pontil -
2022 Poster: Group Meritocratic Fairness in Linear Contextual Bandits »
Riccardo Grazzi · Arya Akhavan · John IF Falk · Leonardo Cella · Massimiliano Pontil -
2021 Poster: A Gang of Adversarial Bandits »
Mark Herbster · Stephen Pasteris · Fabio Vitale · Massimiliano Pontil -
2021 Poster: The Role of Global Labels in Few-Shot Classification and How to Infer Them »
Ruohan Wang · Massimiliano Pontil · Carlo Ciliberto -
2021 Poster: Distributed Zero-Order Optimization under Adversarial Noise »
Arya Akhavan · Massimiliano Pontil · Alexandre Tsybakov -
2020 Poster: Estimating weighted areas under the ROC curve »
Andreas Maurer · Massimiliano Pontil -
2020 Poster: Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning »
Luca Oneto · Michele Donini · Giulia Luise · Carlo Ciliberto · Andreas Maurer · Massimiliano Pontil -
2020 Poster: Fair regression with Wasserstein barycenters »
Evgenii Chzhen · Christophe Denis · Mohamed Hebiri · Luca Oneto · Massimiliano Pontil -
2020 Poster: Fair regression via plug-in estimator and recalibration with statistical guarantees »
Evgenii Chzhen · Christophe Denis · Mohamed Hebiri · Luca Oneto · Massimiliano Pontil -
2020 Oral: Fair regression via plug-in estimator and recalibration with statistical guarantees »
Evgenii Chzhen · Christophe Denis · Mohamed Hebiri · Luca Oneto · Massimiliano Pontil -
2019 Poster: Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification »
Evgenii Chzhen · Christophe Denis · Mohamed Hebiri · Luca Oneto · Massimiliano Pontil -
2018 Poster: Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance »
Giulia Luise · Alessandro Rudi · Massimiliano Pontil · Carlo Ciliberto -
2018 Poster: Empirical Risk Minimization Under Fairness Constraints »
Michele Donini · Luca Oneto · Shai Ben-David · John Shawe-Taylor · Massimiliano Pontil