Set-valued 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.

Minimax semi-supervised set-valued approach to multi-class classification

E. Chzhen; C. Denis, M. Hebiri; Bernoulli (to appear) 2021+.

An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression

E. Chzhen, N. Schreuder; AFCI at NeurIPS 2020.

Quantifying risk-fairness trade-off in regression

E. Chzhen, N. Schreuder; Fair AI in Finance at NeurIPS 2020 (**spotlight**).

A minimax framework for quantifying risk-fairness trade-off in regression

E. Chzhen, N. Schreuder; (submitted; extended version of the above work) 2020.

Fair regression with wasserstein barycenters

E. Chzhen, C. Denis, M. Hebiri, L. Oneto, M. Pontil; NeurIPS 2020.

Fair Regression via Plug-in Estimator and Recalibration With Statistical Guarantees

E. Chzhen, C. Denis, M. Hebiri, L. Oneto, M. Pontil; NeurIPS 2020 (**oral**).

Optimal rates for F-score binary classification

E. Chzhen; Mathematical Methods of Statistics (to appear) 2021+.

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.

Plug-in 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 multi-label classification

E. Chzhen, C. Denis, M. Hebiri, J. Salmon; Technical Report 2017.