Jonas Rothfuss

• Ph.D. Student
• jonas.rothfuss@inf.ethz.ch
• OAT Y 17
• +41 44 632 84 23
• LinkedIn

---

Publications

2024

Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning A. Bhardwaj, J. Rothfuss, B. Sukhija, Y. As, M. Hutter, S. Coros, A. Krause In IEEE Robotics and Automation Letters, IEEE, 2024 [bibtex] [abstract] [pdf]

Bridging the Sim-to-Real Gap with Bayesian Inference J. Rothfuss*, B. Sukhija*, L. Treven*, F. Dörfler, S. Coros, A. Krause In Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2024 Oral presentation [bibtex] [abstract] [pdf]

2023

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice J. Rothfuss, M. Josifoski, V. Fortuin, A. Krause In Journal of Machine Learning Research, 2023 [bibtex] [abstract] [pdf]

Instance-Dependent Generalization Bounds via Optimal Transport S. Hou*, P. Kassraie*, A. Kratsios*, A. Krause, J. Rothfuss* In Journal of Machine Learning Research, 2023 [bibtex] [abstract] [pdf]

Lifelong Bandit Optimization: No Prior and No Regret F. Schur*, P. Kassraie*, J. Rothfuss, A. Krause In Conference on Uncertainty in Artificial Intelligence (UAI), 2023 [bibtex] [abstract] [pdf]

MARS: Meta-Learning as Score Matching in the Function Space K. L. Pavasovic*, J. Rothfuss*, A. Krause In International Conference on Learning Representations (ICLR), 2023 Spotlight presentation [bibtex] [abstract] [pdf]

BaCaDI: Bayesian Causal Discovery with Unknown Interventions A. Hägele, J. Rothfuss, L. Lorch, V. R. Somnath, B. Schölkopf, A. Krause In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Oral presentation [bibtex] [abstract] [pdf]

2022

Amortized Inference for Causal Structure Learning L. Lorch, S. Sussex, J. Rothfuss, A. Krause, B. Schölkopf In Proc. Neural Information Processing Systems (NeurIPS), 2022 [bibtex] [abstract] [pdf] [code]

Meta-Learning Priors for Safe Bayesian Optimization J. Rothfuss, C. Koenig, A. Rupenyan, A. Krause In Proc. Conference on Robot Learning (CoRL), 2022 Oral presentation [bibtex] [abstract] [pdf]

Meta-Learning Hypothesis Spaces for Sequential Decision-making P. Kassraie, J. Rothfuss, A. Krause In Proc. International Conference on Machine Learning (ICML), 2022 [bibtex] [abstract] [pdf]

2021

Meta-Learning Reliable Priors in the Function Space J. Rothfuss, D. Heyn, J. Chen, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2021 [bibtex] [abstract] [pdf] [code]

DiBS: Differentiable Bayesian Structure Learning L. Lorch, J. Rothfuss, B. Schölkopf, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2021 Spotlight presentation [bibtex] [abstract] [pdf] [code] [blog]

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees J. Rothfuss, V. Fortuin, M. Josifoski, A. Krause In Proc. International Conference on Machine Learning (ICML), 2021 [bibtex] [abstract] [pdf] [code]

Robustness to Pruning Predicts Generalization in Deep Neural Networks L. Kuhn, C. Lyle, A. N. Gomez, J. Rothfuss, Y. Gal ArXiv, 2021 [bibtex] [abstract] [pdf]

2019

Forward-looking P M. Ulrich, S. Walther, J. Rothfuss, F. Ferreira In SSRN, 2019 [bibtex] [abstract] [pdf]

Noise Regularization for Conditional Density Estimation J. Rothfuss, F. Ferreira, S. Boehm, S. Walther, M. Ulrich, T. Asfour, A. Krause ArXiv, 2019 [bibtex] [abstract] [pdf] [code]

ProMP: Proximal Meta-Policy Search J. Rothfuss, D. Lee, I. Clavera, T. Asfour, P. Abbeel In International Conference on Learning Representations (ICLR), 2019 [bibtex] [abstract] [pdf]

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks J. Rothfuss, F. Ferreira, S. Walther, M. Ulrich ArXiv, 2019 [bibtex] [abstract] [pdf]

2018

Model-based reinforcement learning via meta-policy optimization I. Clavera, J. Rothfuss, J. Schulman, Y. Fujita, T. Asfour, P. Abbeel In Conference on Robotic Learning (CoRL), PMLR, 2018 [bibtex] [abstract] [pdf]

Deep episodic memory: Encoding, recalling, and predicting episodic experiences for robot action execution J. Rothfuss, F. Ferreira, E. E. Aksoy, Y. Zhou, T. Asfour In IEEE Robotics and Automation Letters, IEEE, volume 3, 2018 [bibtex] [abstract] [pdf]