Jonas Hübotter

Ph.D. Student
jonas.huebotter@inf.ethz.ch
OAT Y 17
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How can one represent uncertainty in complex domains, and how can this be used to make intelligent decisions? In my research, I try to address this question by working on the mathematical foundations of approximate probabilistic inference and sequential decision-making, drawing inspiration from neuroscience and (online) optimization.

2024
Transductive Active Learning: Theory and Applications J. Hübotter, B. Sukhija, L. Treven, Y. As, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2024 Oral presentation at Aligning Reinforcement Learning Experimentalists and Theorists Workshop at ICML [bibtex] [abstract] [pdf]
Active Fine-Tuning of Generalist Policies M. Bagatella, J. Hübotter, G. Martius, A. Krause In arXiv preprint arXiv:2410.05026, 2024 [bibtex] [abstract] [pdf]
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs J. Hübotter, S. Bongni, I. Hakimi, A. Krause In arXiv preprint arXiv:2410.08020, 2024 Oral presentation at Fine-Tuning in Modern Machine Learning Workshop at NeurIPS [bibtex] [abstract] [pdf]
2023
Efficient Exploration in Continuous-time Model-based Reinforcement Learning L. Treven, J. Hübotter, B. Sukhija, F. Dörfler, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2023 [bibtex] [abstract] [pdf]
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization D. Widmer, D. Kang, B. Sukhija, J. Hübotter, A. Krause, S. Coros In Proc. Conference on Robot Learning (CoRL), 2023 [bibtex] [abstract] [pdf]