1 code implementation • 21 Jun 2019 • Robin Vogel, Aurélien Bellet, Stephan Clémençon, Ons Jelassi, Guillaume Papa
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort.
no code implementations • NeurIPS 2016 • Guillaume Papa, Aurélien Bellet, Stephan Clémençon
The problem of predicting connections between a set of data points finds many applications, in systems biology and social network analysis among others.
no code implementations • NeurIPS 2015 • Guillaume Papa, Stéphan Clémençon, Aurélien Bellet
In many learning problems, ranging from clustering to ranking through metric learning, empirical estimates of the risk functional consist of an average over tuples (e. g., pairs or triplets) of observations, rather than over individual observations.
no code implementations • 9 Jan 2015 • Stéphan Clémençon, Patrice Bertail, Emilie Chautru, Guillaume Papa
In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible.