Publications

2020

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

F. Wenzel, J. Snoek, D. Tran, R. Jenatton

NeurIPS, 2020

Oral Presentation (short)

PDF Code Poster Slides Implemented as Edward2 layers

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

2019

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

Scalable Feature Extraction in Confounded Data

F. Wenzel, S. Mandt, M. Kloft

SoCal ML, 2019

Best poster award runner-up

PDF

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

2018

Quasi-Monte Carlo Flows

F. Wenzel, A. Buchholz, S. Mandt

NeurIPS Workshop on Bayesian Deep Learning, 2018

PDF

Fast Inference in Non-Conjugate Gaussian Process Models via Data Augmentation

F. Wenzel, T. Galy-Fajou, C. Donner, M. Kloft, M. Opper

NeurIPS Workshop Bayesian Nonparametrics, 2018

PDF Code

Scalable Multi-Class Gaussian Process Classification via Data Augmentation

T. Galy-Fajou, F. Wenzel, C. Donner, M. Opper

NeurIPS Symposium on Advances in Approximate Bayesian Inference, 2018

PDF Code

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

2017

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

Netflix Travel Award Oral Presentation

PDF

Generalizing and Scaling up Dynamic Topic Models via Inducing Point Variational Inference

P. Jähnichen, F. Wenzel, M. Kloft, S. Mandt

NIPS Workshop on Advances in Approximate Bayesian Inference, 2017

PDF

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

PDF Code

2016

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, 2016

PDF

Scalable Inference in Dynamic Mixture Models

P. Jähnichen, F. Wenzel, M. Kloft, S. Mandt

NIPS Workshop on Advances in Approximate Bayesian Inference, 2016

PDF

Scalable Inference in Dynamic Admixture Models

P. Jähnichen, F. Wenzel, M. Kloft, S. Mandt

LWDA, 2016

Oral Presentation

PDF

Separating Sparse Signals from Correlated Noise in Binary Classification

S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert, M. Kloft

UAI Workshop on Causation: Foundation to Application, 2016

Oral Presenation

PDF

2015

Finding Sparse Features in Strongly Confounded Medical Binary Data

S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert, M. Kloft

NIPS Workshop on Machine Learning in Healthcare, 2015

Oral Presentation

PDF

2014

Probit Regression with Correlated Label Noise: An EM-EP approach

S. Mandt, F. Wenzel, J. P. Cunningham, M. Kloft

NIPS Workshop on Advances in Variational Inference, 2014

PDF