80 citations · 87 across the 12 of their papers we have counts for
13 papers
Polar Depth for Potentially Heavy-Tailed Data
Stephan Clemençon, Carlos Fernándes, Pavlo Mozharovskyi +1
Motivated by the analysis of the behaviour of extremes from multivariate heavy-tailed distributions, we introduce a novel notion of statistical depth, referred to as Polar Depth. T…
Decentralized Ranking Aggregation via Gossip: Convergence and Robustness
Kerrian Le Caillec, Anna Van Elst, Igor Colin +1
The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory,…
Fast and Efficient Asynchronous Gossip Algorithm for Robust and Non-Smooth Convex Decentralized Learning
Anna van Elst, Olivier Fercoq, Igor Colin +1
Asynchronous primal-dual methods for decentralized non-smooth convex optimization often require each node to maintain auxiliary variables, where is its degree.…
TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
Khalid Oublal, Quentin Bouniot, Qi Gan +2
As black box models and pretrained models gain traction in time series applications, understanding and explaining their predictions becomes increasingly vital, especially in high-s…
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Morgane Goibert, Clément Calauzènes, Ekhine Irurozki +1
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed. Prefere…
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki +1
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation of a finite se…