Jakob Weissteiner

jw_stable_diffusion        by    StableDiffusion
Jakob Weissteiner
Ph.D. Candidate
Department of Informatics
University of Zurich
Binzmühlestrasse 14

CH-8050 Zürich

Website jakobweissteiner.com
Room

BIN 2.B.03

Tel +41 44 635 43 32
Email lastName[at]ifi[dot]uzh[dot]ch

 

Research Interests

Machine Learning, Deep Learning, Probabilistic AI, Combinatorial Auctions, Market Design, Preference Elicitation

Short Bio

Since February 2019, Jakob is a Ph.D. student advised by Prof. Sven Seuken in the Computation and Economics Research Group at the Department of Informatics of the University of Zurich, where he works on machine learning-based market design.

Jakob received a B.Sc. (2015) and a M.Sc. (2018) in Mathematics from the Technical University of Vienna (specialization: Financial and Actuarial Mathematics). Additionally he received a M.Sc. (2018) in Quantitative Finance from the Vienna University of Economics and Business.

Since September 2021 he is a ETH AI Center affiliated PhD student.

Besides his studies, Jakob was as Workflow Chair part of the organizing committee of the twenty-third ACM Conference on Economics and Computation (EC'22), he worked in the Advanced Analytics team of the Raiffeisen Bank International as a data scientist. From September 2019 until March 2022, he was a board member of the Club Alpbach Zürich.

Publications

  1. Deep Learning-powered Iterative Combinatorial Auctions. 
    Jakob Weissteiner and Sven Seuken.
    In Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI '20), New York, USA, February 2020.
    Working paper version from Oct 2020: [pdf] [code]
  2. Fourier Analysis-based Iterative Combinatorial Auctions.
    Jakob Weissteiner*, Chris Wendler*, Sven Seuken, Ben Lubin, and Markus Püschel.
    In Proceedings of the Thirty-first International joint Conference on Artificial Intelligence (IJCAI '22), Vienna, AUT, July 2022.
    Full paper version including appendix:  [pdf] [code]
  3. Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
    Jakob Weissteiner*, Jakob Heiss*, Julien Siems* and Sven Seuken.
    In Proceedings of the Thirty-first International joint Conference on Artificial Intelligence (IJCAI '22), Vienna, AUT, July 2022.
    Full paper version including appendix: [pdf] [code]
  4. NOMU: Neural Optimization-based Model Uncertainty
    Jakob Weissteiner*, Hanna Wutte*, Jakob Heiss*, Sven Seuken, and Josef Teichmann.
    In Proceedings of the Thirty-ninth International Conference on Machine Learning (ICML '22), Baltimore, USA, July 2022.
    Full paper version including appendix:  [pdf] [code]
  5. Bayesian Optimization-based Combinatorial Assignment
    Jakob Weissteiner*, Jakob Heiss*, Julien Siems* and Sven Seuken
    In Proceedings of the Thirty-seventh AAAI Conference on Artificial Intelligence (AAAI'23), Washington, D.C., USA, February 2023.
    Full paper version including appendix: [pdf] [code]

*These authors contributed equally

Working Papers

  1. Machine Learning-powered Course Allocation.
    Ermis Soumalias*, Behnoosh Zamanlooy*, Jakob Weissteiner and Sven Seuken.
    ArXiv preprint: [pdf]

Master's Theses

  1. Variable importance measures in classification and regression methods. 
    Jakob Weissteiner. Master's Thesis. Vienna University of Economics and Business, Austria, Sep 2018. [pdf] (PDF, 1 MB)
  2. Über die Orderbuchmodellierung mit Markovschen Ketten in stetiger Zeit.
    Jakob Weissteiner. Master's Thesis. Technical University of Vienna, Austria, Jan 2018. [pdf] (PDF, 2 MB)

Curriculum Vitae

Teaching

2021: Head teaching assistant for lecture Market Design and Machine Learning 
          Head teaching assistant for lecture Seminar: Advanced Topics in Economics and Computation

2020: Head teaching assistant for lecture Seminar: Advanced Topics in Economics and Computation

2019: Teaching assistant for lecture Economics and Computation
          Head teaching assistant for Seminar: Advanced Topics in Economics and Computation.

2018: Teaching assistant for lecture Economics and Computation
          Teaching assistant for lecture Mathematics II, Vienna University of Economics and Business.

2017: Teaching assistant for lecture Probability Theory, Vienna University of Economics and Business.

2016: Teaching assistant for lecture Risk Management in Finance and Insurance, Technical University of Vienna.

Advised Theses

  • Bayesian Optimization with Neural Networks
    Master Thesis by Marius Högger, Fall 2020