Florian Wenzel

Humboldt University of Berlin
Department of Computer Science

Rudower Chaussee 25
Building Section 4, Room 217
12489 Berlin

wenzelfl@hu-berlin.de

Short Bio

Since October 2015, I am a PhD student in the machine learning research group at Humboldt University of Berlin headed by Marius Kloft. 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

  • Our paper Sparse Probit Linear Mixed Model got accepted in Machine Learning. You can check it out here.
  • Our paper Bayesian Nonlinear Support Vector Machines for Big Data got accepted as an oral at ECML.
Last update 07/19/17.

Teaching


Past Courses:

Publications

2017
  • S. Mandt*, F. Wenzel*, S. Nakajima, J. P. Cunningham, C. Lippert, and M. Kloft: Sparse Probit Linear Mixed Model. Machine Learning Journal, Jul 2017, ISSN 1573-0565 . [PDF] [CODE]
    * = equal contribution
  • F. Wenzel, M. Deutsch, T. Galy-Fajou and M. Kloft: Bayesian Nonlinear Support Vector Machines for Big Data. ECML PKDD, 2017. (Oral Presentation) [PDF] [CODE]
2016
  • F. Wenzel, M. Deutsch, T. Galy-Fajou and M. Kloft: Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine. Proceedings of the Workshop on Advances in Approximate Bayesian Inference at NIPS, 2016. [PDF]
  • P. Jähnichen, F. Wenzel and M. Kloft: Scalable Inference in Dynamic Mixture Models. Proceedings of the Workshop on Advances in Approximate Bayesian Inference at NIPS, 2016. [PDF]
  • P. Jähnichen, F. Wenzel, and M. Kloft: Scalable Inference in Dynamic Admixture Models. Proceedings of LWDA, 2016. (Oral Presentation)[PDF]
  • S. Mandt*, F. Wenzel*, S. Nakajima, C. Lippert, and M. Kloft: Separating Sparse Signals from Correlated Noise in Binary Classification. Proceedings of the UAI Workshop Causation: Foundation to Application, 2016. (Oral Presentation) [PDF]
    * = equal contribution
2015 and before
  • S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert, and M. Kloft: Finding Sparse Features in Strongly Confounded Medical Binary Data. NIPS 2015 Workshop on Machine Learning in Healthcare, 2015. (Oral Presentation) [PDF]
  • F. Wenzel: Probit Regression with Correlated Label Noise. Master's thesis, Humboldt University of Berlin, 2015. [PDF]
  • S. Mandt, F. Wenzel, J. Cunningham, and M. Kloft: Probit Regression with Correlated Label Noise: An EM-EP approach. Proceedings of the NIPS workshop on Advances in Variational Inference, 2014. [PDF]