Florian Wenzel

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Stephan MandtAssociate Professor, University of California, IrvineVerified email at uci.edu
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Rodolphe JENATTONSenior machine learning scientist at Google Brain, BerlinVerified email at google.com
Jasper SnoekGoogle BrainVerified email at google.com
Théo Galy-FajouTechnische Universität BerlinVerified email at tu-berlin.de
Dustin TranResearch Scientist, GoogleVerified email at google.com
Bastiaan S. VeelingUniversity of AmsterdamVerified email at uva.nl
Kevin RothETH ZurichVerified email at inf.ethz.ch
Vincent FortuinPostdoctoral researcher, University of CambridgeVerified email at cam.ac.uk
Tim SalimansGoogle Brain AmsterdamVerified email at google.com
Sebastian NowozinPartner Research Manager, Machine Intelligence, Microsoft Research (Cambridge, UK)Verified email at microsoft.com
Linh TranImperial College LondonVerified email at imperial.ac.uk
Jakub SwiatkowskiPhD Student, University of WarsawVerified email at mimuw.edu.pl
John CunninghamProfessor, Columbia UniversityVerified email at columbia.edu
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Christoph LippertProfessor, Hasso Plattner Institute, Universität PotsdamVerified email at hpi.de
Patrick JähnichenPostDoc in Machine Learning, Humboldt-Universität zu BerlinVerified email at hu-berlin.de
Alexander BuchholzAmazon Music MLVerified email at amazon.de
Christian DonnerTechnische Universität BerlinVerified email at bccn-berlin.de
Balaji LakshminarayananStaff Research Scientist at Google BrainVerified email at google.com

Florian Wenzel

Amazon AWS

Verified email at amazon.com - Homepage

Machine Learning Deep Learning Robustness Causal Representation Learning


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How good is the bayes posterior in deep neural networks really? F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ... ICML 2020, 2020	197*	2020
Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton NeurIPS 2020, 2020	86	2020
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt ICML 2018, 2018	47	2018
Bayesian neural network priors revisited V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ... ICRL 2021, 2021	45	2021
Bayesian Nonlinear Support Vector Machines for Big Data F Wenzel, T Galy-Fajou, M Deutsch, M Kloft ECML 2017, 2017	33	2017
Efficient Gaussian process classification using Polya-Gamma data augmentation F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper AAAI Conference on Artificial Intelligence 2019, 2019	28	2019
Scalable Generalized Dynamic Topic Models P Jähnichen, F Wenzel, M Kloft, S Mandt AISTATS 2018, 2018	27	2018
Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021	24	2021
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper UAI 2019, 2019	16	2019
Sparse probit linear mixed model S Mandt, F Wenzel, S Nakajima, J Cunningham, C Lippert, M Kloft Machine Learning 106, 1621-1642, 2017	12	2017
On stein variational neural network ensembles F D'Angelo, V Fortuin, F Wenzel arXiv preprint arXiv:2106.10760, 2021	11	2021
Sparse moes meet efficient ensembles JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ... TMLR, 2022	7	2022
Distilling Ensembles Improves Uncertainty Estimates Z Mariet, R Jenatton, F Wenzel, D Tran Advances in Approximate Bayesian Inference (AABI), 2020	6	2020
Deep classifiers with label noise modeling and distance awareness V Fortuin, M Collier, F Wenzel, J Allingham, J Liu, D Tran, ... TMLR, 2022	4	2022
The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent TGJ Rudner, F Wenzel, YW Teh, Y Gal NeurIPS 2019 Workshop Bayesian Deep Learning Workshop, 2019	4	2019
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models T Galy-Fajou, F Wenzel, M Opper AISTATS 2020, 2020	3	2020
Quasi-Monte Carlo Flows F Wenzel, A Buchholz, S Mandt NeurIPS Bayesian Deep Learning Workshop, 2018	3	2018
Scalable multi-class Gaussian process classification via data augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper NIPS Workshop on Approximate Inference, 1-12, 2018	2	2018
Scalable Logit Gaussian Process Classification F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper NIPS Workshop on Advances in Approximate Bayesian Inference, 2017	1	2017
Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine F Wenzel, M Deutsch, T Galy-Fajou, M Kloft NIPS Workshop on Advances in Approximate Bayesian Inference, 2017	1	2017

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