8 citations · 11 across the 2 of their papers we have counts for
3 papers
Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning
Robin Vogel, Aurélien Bellet, Stephan Clémençon +2
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effor…
Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers
Clémençon Stephan, Patrice Bertail, Guillaume Papa
The generalization ability of minimizers of the empirical risk in the context of binary classification has been investigated under a wide variety of complexity assumptions for the…
Survey schemes for stochastic gradient descent with applications to M-estimation
Stéphan Clémençon, Patrice Bertail, Emilie Chautru +1
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 ha…