• Skip to primary navigation
  • Skip to content
  • Skip to footer
julian-rodemann.de
  • Teaching :man_teacher:
  • Research :thinking:
  • Bio :necktie:
  • Legal :sleeping:
  • Home :house:
    Julian Rodemann

    Julian Rodemann

    PhD Candidate in Statistics and Machine Learning

    • Munich, Germany, :earth_africa:
    • mail[at]julian-rodemann.de
    • University
    • bidt Website
    • Twitter
    • LinkedIn
    • GitHub
    • GitLab
    • Bitbucket
    • Google Scholar
    • Researchgate

    Paper Accepted in Machine Learning :champagne:

    less than 1 minute read

    Our paper “Learning De-Biased Regression Trees and Forests from Complex Samples” has been published in Machine Learning. The paper got accepted at the journal track of the 15th Asian Conference on Machine Learning (ACML 2023) last Summer. Great collaboration with Malte Nalenz and Thomas Augustin!

    Updated: January 8, 2024

    Share on

    Twitter Facebook LinkedIn
    Previous Next

    You may also enjoy

    Our research in the media :newspaper:

    less than 1 minute read

    Our research on personalizing exosuits via interpretable Bayesian optimization was covered by Börsen-Zeitung, Germany’s main newspaper on financial markets.

    Chairing Two Sessions at ECML-PKDD 2025 in Porto

    less than 1 minute read

    I’m on my way to Porto to attend the European Conference on Machine Learning ECML-PKDD (Europe’s flagship conference on Machine Learning) in order to present...

    Started new position at CISPA Helmholtz :rocket:

    less than 1 minute read

    Happy to share that today’s my first day at the Rational Intelligence (RI) Lab at CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Th...

    Paper accepted to EMNLP findings :champagne:

    less than 1 minute read

    Our paper “GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation” has been accepted to the findings track of...

    • Follow:
    • Feed
    © 2025 julian-rodemann.de. Powered by Jekyll & Minimal Mistakes.