Model-Based Reinforcement Learning via Meta-Policy Optimization. - Ignasi Clavera; Jonas Rothfuss; John Schulman; Yasuhiro Fujita 001; Tamim Asfour; Pieter Abbeel (CoRL, 2018)

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets. - Fábio Ferreira; Jonas Rothfuss; Eren Erdal Aksoy; You Zhou 007; Tamim Asfour (CoRR, 2018)

Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution. - Jonas Rothfuss; Fábio Ferreira; Eren Erdal Aksoy; You Zhou 007; Tamim Asfour (IEEE Robotics Autom. Lett., 2018)

ProMP: Proximal Meta-Policy Search. - Jonas Rothfuss; Dennis Lee 003; Ignasi Clavera; Tamim Asfour; Pieter Abbeel (ICLR (Poster), 2019)

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks. - Jonas Rothfuss; Fábio Ferreira; Simon Walther; Maxim Ulrich (CoRR, 2019)

Noise Regularization for Conditional Density Estimation. - Jonas Rothfuss; Fábio Ferreira; Simon Boehm; Simon Walther; Maxim Ulrich; Tamim Asfour; Andreas Krause 001 (CoRR, 2019)

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. - Jonas Rothfuss; Vincent Fortuin; Andreas Krause 001 (CoRR, 2020)

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. - Jonas Rothfuss; Vincent Fortuin; Martin Josifoski; Andreas Krause 001 (ICML, 2021)

DiBS: Differentiable Bayesian Structure Learning. - Lars Lorch; Jonas Rothfuss; Bernhard Schölkopf; Andreas Krause 001 (NeurIPS, 2021)

Meta-Learning Reliable Priors in the Function Space. - Jonas Rothfuss; Dominique Heyn; Jinfan Chen; Andreas Krause 001 (NeurIPS, 2021)

Robustness to Pruning Predicts Generalization in Deep Neural Networks. - Lorenz Kuhn; Clare Lyle; Aidan N. Gomez; Jonas Rothfuss; Yarin Gal (CoRR, 2021)

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. - Yashas Annadani; Jonas Rothfuss; Alexandre Lacoste; Nino Scherrer; Anirudh Goyal; Yoshua Bengio; Stefan Bauer (CoRR, 2021)

Meta-Learning Priors for Safe Bayesian Optimization. - Jonas Rothfuss; Christopher König; Alisa Rupenyan; Andreas Krause 001 (CoRL, 2022)

Meta-Learning Hypothesis Spaces for Sequential Decision-making. - Parnian Kassraie; Jonas Rothfuss; Andreas Krause 001 (ICML, 2022)

Amortized Inference for Causal Structure Learning. - Lars Lorch; Scott Sussex; Jonas Rothfuss; Andreas Krause 001; Bernhard Schölkopf (NeurIPS, 2022)

PAC-Bayesian Meta-Learning: From Theory to Practice. - Jonas Rothfuss; Martin Josifoski; Vincent Fortuin; Andreas Krause 001 (CoRR, 2022)

BaCaDI: Bayesian Causal Discovery with Unknown Interventions. - Alexander Hägele; Jonas Rothfuss; Lars Lorch; Vignesh Ram Somnath; Bernhard Schölkopf; Andreas Krause 001 (AISTATS, 2023)

MARS: Meta-learning as Score Matching in the Function Space. - Krunoslav Lehman Pavasovic; Jonas Rothfuss; Andreas Krause 001 (ICLR, 2023)

Hallucinated adversarial control for conservative offline policy evaluation. - Jonas Rothfuss; Bhavya Sukhija; Tobias Birchler; Parnian Kassraie; Andreas Krause 001 (UAI, 2023)

Lifelong bandit optimization: no prior and no regret. - Felix Schur; Parnian Kassraie; Jonas Rothfuss; Andreas Krause 001 (UAI, 2023)

Instance-Dependent Generalization Bounds via Optimal Transport. - Songyan Hou; Parnian Kassraie; Anastasis Kratsios; Andreas Krause 001; Jonas Rothfuss (J. Mach. Learn. Res., 2023)

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice. - Jonas Rothfuss; Martin Josifoski; Vincent Fortuin; Andreas Krause 001 (J. Mach. Learn. Res., 2023)

Bridging the Sim-to-Real Gap with Bayesian Inference. - Jonas Rothfuss; Bhavya Sukhija; Lenart Treven; Florian Dörfler; Stelian Coros; Andreas Krause 001 (IROS, 2024)

Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning. - Arjun Bhardwaj; Jonas Rothfuss; Bhavya Sukhija; Yarden As; Marco Hutter 001; Stelian Coros; Andreas Krause 001 (IEEE Robotics Autom. Lett., 2024)

Meta-Learning Priors from Limited Data: From Theory to Practice. - Jonas Rothfuss (2024)

SequenceLayers: Sequence Processing and Streaming Neural Networks Made Easy. - R. J. Skerry-Ryan; Julian Salazar; Soroosh Mariooryad; David Kao; Daisy Stanton; Eric Battenberg; Matt Shannon; Ron J. Weiss; Robin Scheibler; Jonas Rothfuss; Tom Bagby (CoRR, 2025)