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2020 – today
- 2024
[i11]Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz:
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. CoRR abs/2407.05704 (2024)- [i10]Evgenii Chzhen, Mohamed Hebiri, Gayane Taturyan:
Regression under demographic parity constraints via unlabeled post-processing. CoRR abs/2407.15453 (2024) - 2023
- [c8]Solenne Gaucher, Nicolas Schreuder, Evgenii Chzhen:
Fair learning with Wasserstein barycenters for non-decomposable performance measures. AISTATS 2023: 2436-2459 - [c7]Evgenii Chzhen, Christophe Giraud, Zhen Li, Gilles Stoltz:
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness. NeurIPS 2023 - [i9]Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder, Vianney Perchet:
Addressing bias in online selection with limited budget of comparisons. CoRR abs/2303.09205 (2023) - [i8]Evgenii Chzhen, Christophe Giraud, Zhen Li, Gilles Stoltz:
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness. CoRR abs/2305.15807 (2023) - 2022
- [c6]Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov:
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback. NeurIPS 2022 - [i7]Solenne Gaucher, Nicolas Schreuder, Evgenii Chzhen:
Fair learning with Wasserstein barycenters for non-decomposable performance measures. CoRR abs/2209.00427 (2022) - 2021
- [c5]Evgenii Chzhen, Christophe Giraud, Gilles Stoltz:
A Unified Approach to Fair Online Learning via Blackwell Approachability. NeurIPS 2021: 18280-18292 - [c4]Nicolas Schreuder, Evgenii Chzhen:
Classification with abstention but without disparities. UAI 2021: 1227-1236 - [i6]Nicolas Schreuder, Evgenii Chzhen
:
Classification with abstention but without disparities. CoRR abs/2102.12258 (2021) - [i5]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Titouan Lorieul:
Set-valued classification - overview via a unified framework. CoRR abs/2102.12318 (2021) - [i4]Evgenii Chzhen, Christophe Giraud, Gilles Stoltz:
A Unified Approach to Fair Online Learning via Blackwell Approachability. CoRR abs/2106.12242 (2021) - 2020
- [c3]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair regression with Wasserstein barycenters. NeurIPS 2020 - [c2]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair regression via plug-in estimator and recalibration with statistical guarantees. NeurIPS 2020 - [i3]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair Regression with Wasserstein Barycenters. CoRR abs/2006.07286 (2020) - [i2]Evgenii Chzhen, Nicolas Schreuder:
An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression. CoRR abs/2011.07158 (2020)
2010 – 2019
- 2019
- [c1]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification. NeurIPS 2019: 12739-12750 - 2017
- [i1]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Joseph Salmon:
On the benefits of output sparsity for multi-label classification. CoRR abs/1703.04697 (2017)
Coauthor Index
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