Florian Wenzel

PhD student in machine learning at Humboldt University of Berlin and TU Kaiserslautern.

wenzelfl@hu-berlin.de

Short Bio

Since October 2015, I am a PhD student in machine learning with Marius Kloft. Our group moved from Humboldt University of Berlin to TU Kaiserslautern, October 2017. I did a scientific internship at Disney Research in Pittsburgh (USA) from February to May 2017. In September 2015, I received a Master's degree (M.Sc.) in mathematics from the Humboldt University of Berlin. I wrote my Master's thesis in the field of probabilistic machine learning.

Research Interests

I am interested in probabilistic machine learning and its applications. In particular, I like:

  • Probabilistic Modeling
  • Scalable Bayesian Inference
  • Probabilistic Programming Languages
  • Gaussian Processes

News

  • Here's a little demo jupyter notebook of how to use GPflow.
  • Our workshop paper Scalable Logit Gaussian Process Classification got selected as contributed talk at the Advances in Approximate Bayesian Inference at NIPS. Wow, I feel very honored to receive the Netflix Travel Award.
Last update 01/18/18.

Teaching


Past Courses:

Publications

2018
  • Quasi-Monte Carlo Variational Inference
    A. Buchholz*, F. Wenzel*, S. Mandt
    Internation Conference on Machine Learning (ICML 2018), to appear
    * = equal contribution
  • Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
    F. Wenzel*, T. Galy-Fajou*, C. Donner, M. Kloft and M. Opper
    arXiv preprint. [PDF] [arXiv]
    * = equal contribution
  • Scalable Generalized Dynamic Topic Models
    P. Jähnichen*, F. Wenzel*, M. Kloft and S. Mandt
    Artificial Intelligence and Statistics (AISTATS 2018). [PDF] [arXiv] [CODE]
    * = equal contribution
2017
  • Scalable Logit Gaussian Process Classification
    F. Wenzel, T. Galy-Fajou, C. Donner, M. Kloft and M. Opper
    NIPS 2017 Workshop on Advances in Approximate Bayesian Inference. [PDF]
    (Netfilx Travel Award / Oral Presentation)
  • Generalizing and Scaling up Dynamic Topic Models via Inducing Point Variational Inference.
    P. Jähnichen, F. Wenzel, M. Kloft and S. Mandt
    NIPS 2017 Workshop on Advances in Approximate Bayesian Inference. [PDF]
  • Sparse Probit Linear Mixed Model
    S. Mandt*, F. Wenzel*, S. Nakajima, J. P. Cunningham, C. Lippert and M. Kloft
    Machine Learning, 106(9), 1621-1642. [PDF] [CODE]
    * = equal contribution
  • Bayesian Nonlinear Support Vector Machines for Big Data
    F. Wenzel, M. Deutsch, T. Galy-Fajou and M. Kloft
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017). [PDF] [CODE]
    (Best Student Paper Award Nomination / Oral Presentation)
2016
  • Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine
    F. Wenzel, M. Deutsch, T. Galy-Fajou and M. Kloft
    NIPS 2016 Workshop on Advances in Approximate Bayesian Inference. [PDF]
  • Scalable Inference in Dynamic Mixture Models
    P. Jähnichen, F. Wenzel and M. Kloft
    NIPS 2016 Workshop on Advances in Approximate Bayesian Inference. [PDF]
  • Scalable Inference in Dynamic Admixture Models
    P. Jähnichen, F. Wenzel and M. Kloft
    Learning, Knowledge, Data, Analytics (LWDA 2016). [PDF]
    (Oral Presentation)
  • Separating Sparse Signals from Correlated Noise in Binary Classification
    S. Mandt*, F. Wenzel*, S. Nakajima, C. Lippert and M. Kloft
    UAI 2016 Workshop on Causation: Foundation to Application. [PDF]
    (Oral Presentation)
    * = equal contribution
2015 and before
  • Finding Sparse Features in Strongly Confounded Medical Binary Data
    S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert and M. Kloft
    NIPS 2015 Workshop on Machine Learning in Healthcare. [PDF]
    (Oral Presentation)
  • Probit Regression with Correlated Label Noise
    F. Wenzel
    Master's thesis, Humboldt University of Berlin, 2015. [PDF]
  • Probit Regression with Correlated Label Noise: An EM-EP approach
    S. Mandt, F. Wenzel, J. Cunningham and M. Kloft
    NIPS 2014 Workshop on Advances in Variational Inference. [PDF]

Activities