Bayesian Nonlinear Support Vector Machines for Big Data. - Florian Wenzel; Théo Galy-Fajou; Matthäus Deutsch; Marius Kloft (ECML/PKDD (1), 2017)

Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. - Florian Wenzel; Théo Galy-Fajou; Christian Donner; Marius Kloft; Manfred Opper (AAAI, 2019)

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. - Théo Galy-Fajou; Florian Wenzel; Christian Donner; Manfred Opper (UAI, 2019)

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. - Théo Galy-Fajou; Florian Wenzel; Manfred Opper (AISTATS, 2020)

Adaptive Inducing Points Selection For Gaussian Processes. - Théo Galy-Fajou; Manfred Opper (CoRR, 2021)

Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. - Théo Galy-Fajou; Valerio Perrone; Manfred Opper (Entropy, 2021)

Latent variable augmentation for approximate Bayesian inference: applications for Gaussian processes. - Théo Galy-Fajou (2023)