NeurIPS'18 Workshop Presentations

This year at NeurIPS in Montreal, I have three workshop papers. Together with my colleagues, I will present at:

AAAI'19 paper accepted

Our new paper Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation got accepted at AAAI in Honolulu, Hawaii. We propose an augmented perspective on a GP classification model based on Polya-Gamma variables. The augmented model is conjugate and inference is much faster than in former approaches. We publish the code in our Julia package AugmentedGaussianProcesses – a library for fast inference in non-conjugate GP models based on data augmentation.

ICML'18 paper accepted

Our paper Quasi-Monte Carlo Variational Inference was accepted at ICML. In this paper we explore the usage of Quasi-Monte Carlo sampling for obtaining low variance gradient estimators for Monte Carlo variational inference.

AISTATS'18 paper accepted

I am happy that our paper Generalized Dynamic Topic Models got accepted at AISTATS in Lanzarote. We generalize the classic topic model to topics which evolve other time. The time dynamics are modeled by Gaussian processes. Choosing different kernels for the GPs allows for discovering different time patterns in the text corpus.

Netflix Travel Award / NIPS'17

Our workshop paper Scalable Logit Gaussian Process Classification got selected as contributed talk at the Advances in Approximate Bayesian Inference Workshop at NIPS. Moreover, I am very happy to receive the Netflix Travel Award.

Selected Publications

More Publications

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


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

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

PDF Code

Recent & Upcoming Talks

More Talks

Uber AI
Feb 11, 2019. San Francisco, USA.
University of Southern California (USC)
Feb 8, 2019. Los Angeles, USA.
University of California, Irvine (UCI)
Feb 6, 2019. Los Angeles, USA.
Conference on Artificial Intelligence (AAAI)
Jan 31, 2019. Honolulu, Hawaii, USA.
Sep 3, 2018. Berlin, Germany.
ICML / Oral Conference Track
Jul 11, 2018. Stockholm, Sweden.
Audi / Audi ML Day
Feb 22, 2018. Ingolstadt, Germany.
Feb 15, 2018. Berlin, Germany.
Brandenburg Technical University / Guest Lecture on Variational Inference
Dec 19, 2017. Cottbus, Germany.
NIPS / Advances in Approximate Bayesian Inference Workshop
Dec 7, 2017. Long Beach, USA.
ECML / Contributed Talk
Sep 22, 2017. Skopje, Macedonia.
ECML / Oral Conference Track
Sep 20, 2017. Skopje, Macedonia.


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


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

Past Courses