Timezone: »
We study the problem of learning an optimal regression function subject to a fairness constraint. It requires that, conditionally on the sensitive feature, the distribution of the function output remains the same. This constraint naturally extends the notion of demographic parity, often used in classification, to the regression setting. We tackle this problem by leveraging on a proxy-discretized version, for which we derive an explicit expression of the optimal fair predictor. This result naturally suggests a two stage approach, in which we first estimate the (unconstrained) regression function from a set of labeled data and then we recalibrate it with another set of unlabeled data. The recalibration step can be efficiently performed via a smooth optimization. We derive rates of convergence of the proposed estimator to the optimal fair predictor both in terms of the risk and fairness constraint. Finally, we present numerical experiments illustrating that the proposed method is often superior or competitive with state-of-the-art methods.
Author Information
Evgenii Chzhen (Université Paris-Saclay, INRIA)
Christophe Denis (Universite Gustave Eiffel)
Mohamed Hebiri (Université Gustave Eiffel)
Luca Oneto (University of Genoa)
Massimiliano Pontil (IIT)
Related Events (a corresponding poster, oral, or spotlight)
-
2020 Oral: Fair regression via plug-in estimator and recalibration with statistical guarantees »
Wed. Dec 9th 02:30 -- 02:45 PM Room Orals & Spotlights: Social/Adversarial Learning
More from the Same Authors
-
2021 Spotlight: A Unified Approach to Fair Online Learning via Blackwell Approachability »
Evgenii Chzhen · Christophe Giraud · Gilles Stoltz -
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 -
2023 Poster: Estimating Koopman operators with sketching to provably learn large scale dynamical systems »
Giacomo Meanti · Antoine Chatalic · Vladimir Kostic · Pietro Novelli · Massimiliano Pontil · Lorenzo Rosasco -
2023 Poster: Transfer learning for atomistic simulations using GNNs and kernel mean embeddings »
John Falk · Luigi Bonati · Pietro Novelli · Michele Parrinello · Massimiliano Pontil -
2023 Poster: Sharp Spectral Rates for Koopman Operator Learning »
Vladimir Kostic · Karim Lounici · Pietro Novelli · Massimiliano Pontil -
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: Concentration inequalities under sub-Gaussian and sub-exponential conditions »
Andreas Maurer · Massimiliano Pontil -
2021 Poster: A Gang of Adversarial Bandits »
Mark Herbster · Stephen Pasteris · Fabio Vitale · Massimiliano Pontil -
2021 Poster: A Unified Approach to Fair Online Learning via Blackwell Approachability »
Evgenii Chzhen · Christophe Giraud · Gilles Stoltz -
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 : Spotlight Talk 1: Quantifying risk-fairness trade-off in regression »
Nicolas Schreuder · Evgenii Chzhen -
2020 Poster: Regression with reject option and application to kNN »
Ahmed Zaoui · Christophe Denis · Mohamed Hebiri -
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 -
2019 : Poster session »
Jindong Gu · Alice Xiang · Atoosa Kasirzadeh · Zhiwei Han · Omar U. Florez · Frederik Harder · An-phi Nguyen · Amir Hossein Akhavan Rahnama · Michele Donini · Dylan Slack · Junaid Ali · Paramita Koley · Michiel Bakker · Anna Hilgard · Hailey Joren · Gonzalo Ramos · Jialin Lu · Jingying Yang · Margarita Boyarskaya · Martin Pawelczyk · Kacper Sokol · Mimansa Jaiswal · Umang Bhatt · David Alvarez-Melis · Aditya Grover · Charles Marx · Sherry Yang · Jingyan Wang · Gökhan Çapan · Hanchen Wang · Steffen Grünewälder · Moein Khajehnejad · Gourab Patro · Russell Kunes · Samuel Deng · Yuanting Liu · Luca Oneto · Mengze Li · Thomas Weber · Stefan Matthes · Duy Patrick Tu -
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 -
2011 Poster: The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers »
Luca Oneto · Davide Anguita · Alessandro Ghio · Sandro Ridella