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stat.ML2026
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…
stat.ML2026
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…
stat.ML2026
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…