Papers
Fair learning with Wasserstein barycenters for nondecomposable performance measures
S. Gaucher, N. Schreuder, E. Chzhen
AISTATS 2023
A minimax framework for quantifying riskfairness tradeoff in regression
E. Chzhen, N. Schreuder Annals of Statistics 2022
A gradient estimator via L1randomization for online zeroorder optimization with two point feedback
A. Akhavan, E. Chzhen, M. Pontil, A. Tsybakov NeurIPS 2022 (spotlight, code)
A unified approach to fair online learning via Blackwell approachability
E. Chzhen, C. Giraud, G. Stoltz NeurIPS 2021 (spotlight)
Setvalued classification  overview via a unified framework
E. Chzhen, C. Denis, M. Hebiri, T, Lorieul Preprint 2021
Classification with abstention but without disparities
N. Schreuder, E. Chzhen UAI 2021 (spotlight, code)
Minimax semisupervised setvalued approach to multiclass classification
E. Chzhen, C. Denis, M. Hebiri Bernoulli 2021 (code)
Optimal Rates for Nonparametric FScore Binary Classification via PostProcessing
E. Chzhen Mathematical Methods of Statistics 2021
An example of prediction which complies with Demographic Parity and equalizes groupwise risks in the context of regression
E. Chzhen, N. Schreuder AFCI at NeurIPS 2020
Fair regression with wasserstein barycenters
E. Chzhen, C. Denis, M. Hebiri, L. Oneto, M. Pontil NeurIPS 2020 (code)
Fair Regression via Plugin Estimator and Recalibration With Statistical Guarantees
E. Chzhen, C. Denis, M. Hebiri, L. Oneto, M. Pontil NeurIPS 2020 (oral, code)
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
E. Chzhen, C. Denis, M. Hebiri, L. Oneto, M. Pontil NeurIPS 2019
On Lasso refitting strategies
E. Chzhen, M. Hebiri, J. Salmon Bernoulli 2019
Plugin methods in classification
E. Chzhen PhD manuscript 2019
Classification of sparse binary vectors
E. Chzhen Technical Report 2019
On the benefits of output sparsity for multilabel classification
E. Chzhen, C. Denis, M. Hebiri, J. Salmon Technical Report 2017
