Selected Publications

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A data augmentation perspective on diffusion models and retrieval
Max F Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell
arXiv, 2023

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Leveraging sparse and shared feature activations for disentangled representation learning
Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello
arXiv, 2023

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Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
ICML, 2023

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Evaluating the fairness of discriminative foundation models in computer vision
Junaid Ali, Matthaeus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell
AIES, 2023

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Are Multimodal Models Robust to Image and Text Perturbations?
Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li
arXiv, 2022

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Assaying out-of-distribution generalization in transfer learning
Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello
NeurIPS, 2022

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Deep classifiers with label noise modeling and distance awareness
Vincent Fortuin, Mark Collier, Florian Wenzel, James Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
TMLR, 2021

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Sparse moes meet efficient ensembles
James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
TMLR, 2021

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Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W Dusenberry, Sebastian Farquhar, Qixuan Feng, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim GJ Rudner, Faris Sbahi, Yeming Wen, Florian Wenzel, Kevin Murphy, D Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
arXiv, 2021

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Bayesian Neural Network Priors Revisited
V. Fortuin, A. Garriga-Alonso, Florian Wenzel, G. Rätsch, R. Turner, M. v.d. Wilk, L. Aitchison
ICRL, 2021

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Hyperparameter Ensembles for Robustness and Uncertainty Quantification
F. Wenzel, J. Snoek, D. Tran, R. Jenatton
NeurIPS, 2020
Oral Presentation (short)

PDF Code Poster Slides Edward2 layer Tutorial

How Good is the Bayes Posterior in Deep Neural Networks Really?
F. Wenzel*, K. Roth*, B. Veeling*, J. Świątkowski, L. Tran, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, S. Nowozin (* = equal contribution)
ICML, 2020
Oral Presentation (long)

PDF Code Slides Video

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
T. Galy-Fajou, F. Wenzel, M. Opper
AISTATS, 2020
Oral Presentation

PDF Code

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
T. Galy-Fajou*, F. Wenzel*, C. Donner, M. Opper (* = equal contribution)
UAI, 2019

PDF Code ArXiv

Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
F. Wenzel*, T. Galy-Fajou*, C. Donner, M. Kloft, M. Opper (* = equal contribution)
AAAI, 2019
Oral Presentation

PDF Code ArXiv

Quasi-Monte Carlo Variational Inference
A. Buchholz*, F. Wenzel*, S. Mandt (* = equal contribution)
ICML, 2018
Oral Presentation

PDF ArXiv

Scalable Generalized Dynamic Topic Models
P. Jähnichen*, F. Wenzel*, M. Kloft, S. Mandt (* = equal contribution)
AISTATS, 2018

PDF Code ArXiv

Sparse Probit Linear Mixed Model
S. Mandt*, F. Wenzel*, S. Nakajima, J. P. Cunningham, C. Lippert, M. Kloft (* = equal contribution)
Machine Learning, 2017

PDF Code Journal

Bayesian Nonlinear Support Vector Machines for Big Data
F. Wenzel, T. Galy-Fajou, M. Deutsch, M. Kloft
ECML, 2017
Best Student Paper Award Nomination Oral Presentation

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More Publications

Recent & Upcoming Talks

More Talks

Berlin Bayesians Seminar
Apr 28, 2021. Virtual.
Berlin Machine Learning Seminar (BML)
Feb 17, 2021. Virtual.
University of California, Irvine (UCI) / AIML Seminar
Jan 11, 2021. Virtual.
NeurIPS / Short Teaser Talk for Poster
Dec 10, 2020. Virtual.
Microsoft Research Cambridge
Dec 3, 2020. Virtual.
Google Brain
Nov 30, 2020. Virtual.
ETH Zurich
Nov 24, 2020. Virtual.
ICML / Long Oral Conference Track
Jul 14, 2020. Virtual.
Google Brain Zurich
Jul 2, 2019. Zurich, Switzerland.
TU München
Jun 25, 2019. München, Germany.
University of Oxford
Jun 11, 2019. Oxford, UK.
Hasso Plattner Institut
Mar 21, 2019. Potsdam, Germany.
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Teaching

Supervised Students

  • Lorenz Vaitl: Master’s thesis (TU Berlin, 2018)
    • Scalable Inference for Correlated Noise Classification Models
  • Eren Sezener: Lab rotation project (TU Berlin, 2018)
    • Multi-armed bandits and knowledge gradients

Courses

  • Probabilistic Machine Learning (Supervision of student projects, TU Berlin, Winter 18 / 19)

Past Courses

Contact