Théo Galy-Fajou
Théo Galy-Fajou
Technische Universität Berlin
Verified email at epfl.ch
Title
Cited by
Cited by
Year
Bayesian Nonlinear Support Vector Machines for Big Data
F Wenzel, T Galy-Fajou, M Deutsch, M Kloft
ECML 2017, 2017
212017
Efficient Gaussian process classification using Pòlya-Gamma data augmentation
F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper
AAAI 19, 2018
122018
Multi-class gaussian process classification made conjugate: Efficient inference via data augmentation
T Galy-Fajou, F Wenzel, C Donner, M Opper
arXiv preprint arXiv:1905.09670, 2019
32019
Scalable Multi-Class Gaussian Process Classification via Data Augmentation
T Galy-Fajou, F Wenzel, C Donner, M Opper
Proc. NIPS Workshop Approx. Inference, 1-12, 2018
12018
Scalable logit gaussian process classification
F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper
Advances in Approximate Bayesian Inference, NIPS Workshop, 2017
12017
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
T Galy-Fajou, F Wenzel, M Opper
arXiv preprint arXiv:2002.11451, 2020
2020
Fast Inference in Non-Conjugate Gaussian Process Models via Data Augmentation
F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper
Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine
F Wenzel, M Deutsch, T Galy-Fajou, M Kloft
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Articles 1–8