Florian Wenzel

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Citations	928	923
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Stephan MandtAssociate Professor, University of California, IrvineVerified email at uci.edu
Rodolphe JENATTONSenior machine learning scientist at Google Brain, BerlinVerified email at google.com
Marius KloftRPTU Kaiserslautern-LandauVerified email at cs.uni-kl.de
Dustin TranResearch Scientist, GoogleVerified email at google.com
Jasper SnoekGoogle BrainVerified email at google.com
Théo Galy-FajouTechnische Universität BerlinVerified email at tu-berlin.de
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 NowozinGoogle DeepMindVerified 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
Alexander BuchholzAmazon Music MLVerified email at amazon.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
Chris RussellOxford Internet InstituteVerified email at oii.ox.ac.uk
Francesco LocatelloAssistant Professor, ISTAVerified email at ista.ac.at

Florian Wenzel

Amazon AWS

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Robust Deep Learning Foundation Models Transformers


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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	290	2020
Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton NeurIPS 2020, 2020	164	2020
Bayesian neural network priors revisited V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ... ICRL 2021, 2021	96	2021
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	67	2021
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt ICML 2018, 2018	60	2018
Scalable Generalized Dynamic Topic Models P Jähnichen, F Wenzel, M Kloft, S Mandt AISTATS 2018, 2018	37	2018
Bayesian Nonlinear Support Vector Machines for Big Data F Wenzel, T Galy-Fajou, M Deutsch, M Kloft ECML 2017, 2017	37	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	36	2019
Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022	29	2022
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper UAI 2019, 2019	24	2019
On stein variational neural network ensembles F D'Angelo, V Fortuin, F Wenzel arXiv preprint arXiv:2106.10760, 2021	19	2021
Sparse moes meet efficient ensembles JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ... TMLR, 2022	12	2022
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
Deep classifiers with label noise modeling and distance awareness V Fortuin, M Collier, F Wenzel, J Allingham, J Liu, D Tran, ... TMLR, 2022	9	2022
Are Multimodal Models Robust to Image and Text Perturbations? J Qiu, Y Zhu, X Shi, F Wenzel, Z Tang, D Zhao, B Li, M Li arXiv preprint arXiv:2212.08044, 2022	8	2022
Distilling Ensembles Improves Uncertainty Estimates Z Mariet, R Jenatton, F Wenzel, D Tran Advances in Approximate Bayesian Inference (AABI), 2020	5	2020
Quasi-Monte Carlo Flows F Wenzel, A Buchholz, S Mandt NeurIPS Bayesian Deep Learning Workshop, 2018	5	2018
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
A data augmentation perspective on diffusion models and retrieval MF Burg, F Wenzel, D Zietlow, M Horn, O Makansi, F Locatello, C Russell arXiv preprint arXiv:2304.10253, 2023	2	2023

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