| Bayesian Nonlinear Support Vector Machines for Big Data F Wenzel, T Galy-Fajou, M Deutsch, M Kloft ECML 2017, 2017 | 23 | 2017 |
| Efficient Gaussian process classification using Pòlya-Gamma data augmentation F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper AAAI 19, 2018 | 17 | 2018 |
| Multi-class gaussian process classification made conjugate: Efficient inference via data augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper UAI 2019, 2019 | 7 | 2019 |
| 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 | 1 | 2018 |
| Scalable logit gaussian process classification F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper Advances in Approximate Bayesian Inference, NIPS Workshop, 2017 | 1 | 2017 |
| Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models T Galy-Fajou, F Wenzel, M Opper International Conference on Artificial Intelligence and Statistics, 3025-3035, 2020 | | 2020 |
| Evidence Estimation by Kullback-Leibler Integration for Flow-Based Methods N Zaki, T Galy-Fajou, M Opper | | |
| Gaussian Density Parametrization Flow: Particle and Stochastic Approaches T Galy-Fajou, V Perrone, M Opper | | |
| 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 | | |
Florian WenzelGoogle Brain BerlinBestätigte E-Mail-Adresse bei google.com
