13 citations · 20 across the 8 of their papers we have counts for
8 papers
Addressing Bias in Online Selection with Limited Budget of Comparisons
Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder +1
Consider a hiring process with candidates coming from different universities. It is easy to order candidates with the same background, yet it can be challenging to compare them oth…
Fair learning with Wasserstein barycenters for non-decomposable performance measures
Solenne Gaucher, Nicolas Schreuder, Evgenii Chzhen
This work provides several fundamental characterizations of the optimal classification function under the demographic parity constraint. In the awareness framework, akin to the cla…
Nyström Kernel Mean Embeddings
Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi +1
Kernel mean embeddings are a powerful tool to represent probability distributions over arbitrary spaces as single points in a Hilbert space. Yet, the cost of computing and storing…
Classification with abstention but without disparities
Nicolas Schreuder, Evgenii Chzhen
Classification with abstention has gained a lot of attention in recent years as it allows to incorporate human decision-makers in the process. Yet, abstention can potentially ampli…
An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression
Evgenii Chzhen, Nicolas Schreuder
Let be a triplet following some joint distribution with feature vector , sensitive attribute , an…
Statistical guarantees for generative models without domination
Nicolas Schreuder, Victor-Emmanuel Brunel, Arnak Dalalyan
In this paper, we introduce a convenient framework for studying (adversarial) generative models from a statistical perspective. It consists in modeling the generative device as a s…
A minimax framework for quantifying risk-fairness trade-off in regression
Evgenii Chzhen, Nicolas Schreuder
We propose a theoretical framework for the problem of learning a real-valued function which meets fairness requirements. This framework is built upon the notion of -relative (fa…
A nonasymptotic law of iterated logarithm for general M-estimators
Victor-Emmanuel Brunel, Arnak S. Dalalyan, Nicolas Schreuder
M-estimators are ubiquitous in machine learning and statistical learning theory. They are used both for defining prediction strategies and for evaluating their precision. In this p…