[Statlist] ETH Young Data Science Researcher Seminar Zurich, Virtual Seminar by Evgenii Chzhen, Université Paris-​Saclay

[Maurer Letizia]

Dear all

We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich

"A minimax framework for quantifying risk-​fairness trade-​off in regression"  
by Evgenii Chzhen, Université Paris-​Saclay

Time: Friday, 19 February 2021, 15:00-​16:00
Place: Zoom at https://ethz.zoom.us/j/92367940258

Abstract: A theoretical framework is proposed for the problem of learning a real-​valued function which meets fairness requirements. This framework is built upon the notion of alpha-​relative (fairness) improvement of the regression function which we introduce using the theory of optimal transport. Setting alpha = 0 corresponds to the regression problem under the Demographic Parity constraint, while alpha = 1 corresponds to the classical regression problem without any constraints. For alpha in-​between 0 and 1 the proposed framework allows to continuously interpolate between these two extreme cases and to study partially fair predictors. Within this framework we precisely quantify the cost in risk induced by the introduction of the fairness constraint. We put forward a statistical minimax setup and derive a general problem-​dependent lower bound on the risk of any estimator satisfying alpha-​relative improvement constraint. We illustrate our framework on a model of linear regression with Gaussian design and systematic group-​dependent bias, deriving matching (up to absolute constants) upper and lower bounds on the minimax risk under the introduced constraint. This talk is based on a joint work with Nicolas Schreuder, see [arXiv:2007.14265].

Best wishes,

M. Azadkia, Y. Chen, G. Chinot, M. Löffler, A. Taeb

Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html