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
Vincent FortuinResearch group leader, Helmholtz AIVerified email at helmholtz-muenchen.de
Bastiaan S. VeelingMicrosoft Research AmsterdamVerified email at uva.nl
Kevin RothETH ZurichVerified email at inf.ethz.ch
Tim SalimansGoogle Brain AmsterdamVerified email at google.com
Sebastian NowozinDeepMindVerified email at deepmind.com
Linh TranImperial College LondonVerified email at imperial.ac.uk
Jakub SwiatkowskiAmazonVerified email at amazon.com
Patrick JähnichenPostDoc in Machine Learning, Humboldt-Universität zu BerlinVerified email at hu-berlin.de
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
Alexander BuchholzAmazon Music MLVerified email at amazon.de
Christian DonnerEidgenössische Technische Hochschule ZürichVerified email at ethz.ch
Balaji LakshminarayananSenior Staff 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	254	2020
Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton NeurIPS 2020, 2020	128	2020
Bayesian neural network priors revisited V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ... ICRL 2021, 2021	74	2021
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt ICML 2018, 2018	54	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	51	2021
Bayesian Nonlinear Support Vector Machines for Big Data F Wenzel, T Galy-Fajou, M Deutsch, M Kloft ECML 2017, 2017	37	2017
Scalable Generalized Dynamic Topic Models P Jähnichen, F Wenzel, M Kloft, S Mandt AISTATS 2018, 2018	34	2018
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	31	2019
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper UAI 2019, 2019	21	2019
On stein variational neural network ensembles F D'Angelo, V Fortuin, F Wenzel arXiv preprint arXiv:2106.10760, 2021	14	2021
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
Sparse moes meet efficient ensembles JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ... TMLR, 2022	11	2022
Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022	10	2022
Deep classifiers with label noise modeling and distance awareness V Fortuin, M Collier, F Wenzel, J Allingham, J Liu, D Tran, ... TMLR, 2022	7	2022
Distilling Ensembles Improves Uncertainty Estimates Z Mariet, R Jenatton, F Wenzel, D Tran Advances in Approximate Bayesian Inference (AABI), 2020	7	2020
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models T Galy-Fajou, F Wenzel, M Opper AISTATS 2020, 2020	4	2020
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
Quasi-Monte Carlo Flows F Wenzel, A Buchholz, S Mandt NeurIPS Bayesian Deep Learning Workshop, 2018	4	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

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