Differentiating the Multipoint Expected Improvement for Optimal Batch Design. - Sébastien Marmin; Clément Chevalier; David Ginsbourger (MOD, 2015)

Variational Calibration of Computer Models. - Sébastien Marmin; Maurizio Filippone (CoRR, 2018)

Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations. - Sébastien Marmin; David Ginsbourger; Jean Baccou; Jacques Liandrat (SIAM/ASA J. Uncertain. Quantification, 2018)

Efficient Approximate Inference with Walsh-Hadamard Variational Inference. - Simone Rossi 001; Sébastien Marmin; Maurizio Filippone (CoRR, 2019)

Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units. - Ruben Ohana; Jonas Wacker; Jonathan Dong; Sébastien Marmin; Florent Krzakala; Maurizio Filippone; Laurent Daudet (ICASSP, 2020)

Walsh-Hadamard Variational Inference for Bayesian Deep Learning. - Simone Rossi 001; Sébastien Marmin; Maurizio Filippone (NeurIPS, 2020)

Input uncertainty propagation through trained neural networks. - Paul Monchot; Loic Coquelin; Sébastien Julien Petit; Sébastien Marmin; Erwan Le Pennec; Nicolas Fischer (ICML, 2023)