Jonas Hübotter Ph.D. Student jonas.huebotter@inf.ethz.ch OAT Y 17 Linkedln External Website My current research aims to leverage foundation models for solving hard tasks through specialization and reinforcement learning. Beyond this, I have broad interests including (approximate) probabilistic inference, optimization, and online learning. Publications 2025 Active Fine-Tuning of Multi-Task Policies M. Bagatella, J. Hübotter, G. Martius, A. KrauseIn Proc. International Conference on Machine Learning (ICML), 2025[bibtex] [abstract] [pdf] LITE: Efficiently Estimating Gaussian Probability of Maximality N. Menet, J. Hübotter, P. Kassraie, A. KrauseIn Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2025[bibtex] [abstract] [pdf] Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs J. Hübotter, S. Bongni, I. Hakimi, A. KrauseIn Proc. International Conference on Learning Representations (ICLR), 2025Best paper award at Fine-Tuning in Modern Machine Learning Workshop at NeurIPS[bibtex] [abstract] [pdf] 2024 Transductive Active Learning: Theory and Applications J. Hübotter, B. Sukhija, L. Treven, Y. As, A. KrauseIn Proc. Neural Information Processing Systems (NeurIPS), 2024Oral presentation at Aligning Reinforcement Learning Experimentalists and Theorists Workshop at ICML[bibtex] [abstract] [pdf] 2023 Efficient Exploration in Continuous-time Model-based Reinforcement Learning L. Treven, J. Hübotter, B. Sukhija, F. Dörfler, A. KrauseIn 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. CorosIn Proc. Conference on Robot Learning (CoRL), 2023[bibtex] [abstract] [pdf]
