Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent. - Guillaume Papa; Pascal Bianchi; Stéphan Clémençon (ALT, 2015)

Scalability of Stochastic Gradient Descent based on "Smart" Sampling Techniques. - Stéphan Clémençon; Aurélien Bellet; Ons Jelassi; Guillaume Papa (INNS Conference on Big Data, 2015)

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk. - Guillaume Papa; Stéphan Clémençon; Aurélien Bellet (NIPS, 2015)

Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers. - Stéphan Clémençon; Patrice Bertail; Guillaume Papa (ACML, 2016)

On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. - Guillaume Papa; Aurélien Bellet; Stéphan Clémençon (NIPS, 2016)

Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon; Ons Jelassi; Guillaume Papa (ECML/PKDD (2), 2019)