Jonas Hübotter
Doctoral Researcher at ETH Zurich. I work on Active Few-Shot Learning.
I am a doctoral researcher in the Learning and Adaptive Systems Group at ETH Zurich working with Andreas Krause. Prior to this, I obtained a Master’s degree in Theoretical Computer Science and Machine Learning from ETH Zurich and a Bachelor’s degree in Computer Science and Mathematics from the Technical University of Munich. As an intern at Citadel Securities, I have previously worked with Guillaume Basse and Sören Künzel on time-series prediction. I am a recipient of the ETH Medal.
My research aims to improve the performance of foundation models by utilizing tools from active learning for few-shot learning, active inference, and adaptive computation. Beyond this, I have broad interests including (approximate) probabilistic inference, optimization, and online learning.
Always feel free to reach out to me with things you find exciting.
Contacts: jhuebotter@ethz.ch Google Scholar GitHub Linkedin
Announcements
| Jun, 2024 | Our work on Transductive Active Learning with Application to Safe Bayesian Optimization was accepted as an oral presentation (top 5%) at the Workshop on Aligning RL Experimentalists and Theorists at ICML 2024. |
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| Mar, 2024 | Our work on Active Few-Shot Fine-Tuning was accepted at the Workshop on Bridging the Gap Between Practice and Theory in Deep Learning at ICLR 2024! |
Selected Publications
Supervision
I supervise students at Bachelor's and Master's level. You can find a list of potential projects here. If you are interested in working with me, please reach out.
Fun Projects
Algorithms for online convex optimization with an associated cost for movement in the decision space. Useful for resource allocation, contextual sequence prediction, portfolio management, and object tracking.
Algorithms for online convex optimization with an associated cost for movement in the decision space. Useful for resource allocation, contextual sequence prediction, portfolio management, and object tracking.
Solutions to a wide variety of competitive programming-type questions.
A serverless web app to organize and stream media from anywhere.
Website of a landscape architecture firm.