Julian Rodemann

PhD Candidate in Statistics and Machine Learning

Hi there! :wave:

I am a 3rd year PhD Candidate supervised by Thomas Augustin at the Department of Statistics at Ludwig-Maximilians-Universität (LMU) Munich. I am currently at the Department of Statistics at Harvard University, working with James Bailie under the supervision of Xiao-Li Meng. If you’re in the area and would like to meet, do not hesitate to reach out!

My research revolves around optimization, online learning, and decision theory. I try to render such methods more reliable by representing the involved uncertainties, heavily relying on imprecise probabilities. I am also interested in interpretable machine learning and anything Bayesian. My research has been published in Q1 journals (Machine Learning, Knowledge-Based Systems) and accepted at A*/A conferences (NeurIPS, ICLR, UAI, EMNLP).

I enjoy teaching a lot and teach several (under)graduate classes at our department and co-supervise bachelor and master theses. Potential thesis topics under my supervision can be found here. Besides, I do academic student counselling in our Bachelor’s program “Statistics and Data Science”.

I am part of the the Graduate Center of the Bavarian Research Institute for Digital Transformation (bidt) within the Bavarian Academy of Sciences (BAS). The graduate center is a great place to learn about the many aspects of a digital world that are beyond my own research on machine learning and statistics/data science. My research was partly supported by the Federal Statistical Office of Germany within the cooperation “Machine Learning in Official Statistics”.

Recent posts

Two papers accepted at NeurIPS :champagne: :champagne:

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Pretty excited that both of my first-ever NeurIPS submissions got accepted, one of which as spotlight! This was a great team effort with Christoph Jansen, Ge...

Paper Accepted at EMNLP :champagne:

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Happy to share that our paper “Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation” has been accepted for EMNLP 2024 Find...

Research Stay at Harvard University :us:

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I am spending this fall at Harvard University in Cambridge (MA, USA) to work with James Bailie and Xiao-Li Meng. We will investigate connections between onli...

Talk at ETH Zürich :switzerland:

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I am giving a talk on reciprocal learning at the Seminar für Statistik (SfS), ETH Zürich on August 14th as part of the SfS-PhD Presentations. Drop me a messa...

Paper Accepted at Knowledge-based Systems :champagne:

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Great news! Our paper “Imprecise Bayesian Optimization” has been accepted for publication in Knowledge-based Systems! This is joint work with my supervisor T...

Paper Accepted at DMLR@ICML Workshop :tada:

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Happy to share that the paper “Bayesian Data Selection” has been accepted at the 5th Workshop on Data-Centric Machine Learning (DMLR) at the International Co...

Paper Accepted at SMPS :tada:

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Our paper “Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision” has been accepted at the 11th International Conference on Soft ...

On my way to ICLR 2024 :train2:

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I will attend ICLR 2024 to present our paper “Partial Rankings of Optimizers”, which has been accepted at the ICLR 2024 Tiny Papers Track. Great collaboratio...

Tiny Paper Accepted at ICLR :champagne:

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Our paper “Partial Rankings of Optimizers” has been accepted for presentation at the ICLR 2024 Tiny Papers Track. Great collaboration with Hannah Blocher (eq...

Attending WUML in Munich :busts_in_silhouette:

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I will attend the Workshop on Uncertainty in Machine Learning (WUML) in Munich, organized by Eyke Hüllermeier’s Chair. If you are also attending and want to ...

Paper Accepted in Machine Learning :champagne:

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Our paper “Learning De-Biased Regression Trees and Forests from Complex Samples” has been published in Machine Learning. The paper got accepted at the journa...

Talk and Poster at ACML 2023 in Istanbul

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I will give a talk and present a poster on our recent work on de-biased regression trees and random forests at the 15th Asian Conference on Machine Learning ...

Giving a Talk at BAYSM

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Looking forward to attending this year’s BAyesian Young Statisticians Meeting (BAYSM) hosted by the University of Connecticut, where my short paper has been ...

Talk at KI 2023 in Berlin

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Giving a talk on our recent work on Bayesian pseudo-label selection at the 46th German Conference on Artificial Intelligence (KI 2023) in Berlin. See the con...

Poster at AABI

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Our poster on how to select pseudo-labels in a Bayesian way (paper @UAI) was accepted for the Fifth Symposium on Advances in Approximate Bayesian Inference A...

Two Papers Accepted at UAI :champagne::champagne:

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Thrilled to annouce that our papers “Approximately Bayes-Optimal Pseudo-Label Selection” and “Robust Statistical Comparison of Random Variables with Locally ...

Paper Accepted at ISIPTA :champagne:

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Glad to share that our paper on robust pseudo-label selection was accepted for the 13th International Symposium on Imprecise Probabilities: Theories and Appl...

Paper Accepted at SUM :champagne:

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Very happy to share that our paper on cautious superset learning with applications to election polls was accepted for the 15th International Conference on Sc...

Paper Accepted at IUKM :champagne:

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Our paper “Accounting for Gaussian Process Imprecision in Bayesian Optimization” was accepted at the 9th International Symposium on Integrated Uncertainty in...

Under Construction

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This site is currently under construction. Stay tuned for updates. You might also want to visit my LMU Website.