4 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
A Comprehensive View of Fairness through Distributional Stability
Gayane Taturyan, Charlotte Laclau, Stephan Clémencon
We view fairness as a property of distributional stability. Rather than assessing a predictor under a fixed data distribution, we study how its predictions change under perturbatio…
Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization
Louise Davy, Stephan Clémençon, Charlotte Laclau
Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes…
On Gossip Algorithms for Machine Learning with Pairwise Objectives
Igor Colin, Aurélien Bellet, Stephan Clémençon +1
In the IoT era, information is more and more frequently picked up by connected smart sensors with increasing, though limited, storage, communication and computation abilities. Whet…
Active Bipartite Ranking with Smooth Posterior Distributions
James Cheshire, Stephan Clémençon
In this article, bipartite ranking, a statistical learning problem involved in many applications and widely studied in the passive context, is approached in a much more general \te…
Beyond Kemeny Medians: Consensus Ranking Distributions Definition, Properties and Statistical Learning
Stephan Clémençon, Ekhine Irurozki
In this article we develop a new method for summarizing a ranking distribution, \textit{i.e.} a probability distribution on the symmetric group , beyond the classic…