Jonas Rothfuss

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 36

Top Topics

Hypothesis Space

Causal Structure

Base Classifiers

Top Venues

IEEE Robotics Autom. Lett.

Publications

Arjun Bhardwaj, Jonas Rothfuss, Bhavya Sukhija, Yarden As, Marco Hutter, Stelian Coros, Andreas Krause
Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning. IEEE Robotics Autom. Lett. 9 (4) (2024)
Jonas Rothfuss, Bhavya Sukhija, Lenart Treven, Florian Dörfler, Stelian Coros, Andreas Krause
Bridging the Sim-to-Real Gap with Bayesian Inference. CoRR (2024)
Arjun Bhardwaj, Jonas Rothfuss, Bhavya Sukhija, Yarden As, Marco Hutter, Stelian Coros, Andreas Krause
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning. CoRR (2023)
Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. AISTATS (2023)
Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause
Hallucinated Adversarial Control for Conservative Offline Policy Evaluation. CoRR (2023)
Krunoslav Lehman Pavasovic, Jonas Rothfuss, Andreas Krause
MARS: Meta-learning as Score Matching in the Function Space. ICLR (2023)
Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause
Hallucinated adversarial control for conservative offline policy evaluation. UAI (2023)
Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause
Lifelong bandit optimization: no prior and no regret. UAI (2023)
Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause
PAC-Bayesian Meta-Learning: From Theory to Practice. CoRR (2022)
Jonas Rothfuss, Christopher Koenig, Alisa Rupenyan, Andreas Krause
Meta-Learning Priors for Safe Bayesian Optimization. CoRL (2022)
Krunoslav Lehman Pavasovic, Jonas Rothfuss, Andreas Krause
MARS: Meta-Learning as Score Matching in the Function Space. CoRR (2022)
Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause
Lifelong Bandit Optimization: No Prior and No Regret. CoRR (2022)
Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf
Amortized Inference for Causal Structure Learning. CoRR (2022)
Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. CoRR (2022)
Parnian Kassraie, Jonas Rothfuss, Andreas Krause
Meta-Learning Hypothesis Spaces for Sequential Decision-making. ICML (2022)
Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf
Amortized Inference for Causal Structure Learning. NeurIPS (2022)
Songyan Hou, Parnian Kassraie, Anastasis Kratsios, Jonas Rothfuss, Andreas Krause
Instance-Dependent Generalization Bounds via Optimal Transport. CoRR (2022)
Parnian Kassraie, Jonas Rothfuss, Andreas Krause
Meta-Learning Hypothesis Spaces for Sequential Decision-making. CoRR (2022)
Jonas Rothfuss, Christopher Koenig, Alisa Rupenyan, Andreas Krause
Meta-Learning Priors for Safe Bayesian Optimization. CoRR (2022)
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. CoRR (2021)
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. ICML (2021)
Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause
DiBS: Differentiable Bayesian Structure Learning. CoRR (2021)
Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause
DiBS: Differentiable Bayesian Structure Learning. NeurIPS (2021)
Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause
Meta-Learning Reliable Priors in the Function Space. CoRR (2021)
Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause
Meta-Learning Reliable Priors in the Function Space. NeurIPS (2021)
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal
Robustness to Pruning Predicts Generalization in Deep Neural Networks. CoRR (2021)
Jonas Rothfuss, Vincent Fortuin, Andreas Krause
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. CoRR (2020)
Jonas Rothfuss, Fábio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause
Noise Regularization for Conditional Density Estimation. CoRR (2019)
Jonas Rothfuss, Fábio Ferreira, Simon Walther, Maxim Ulrich
Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks. CoRR (2019)
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel
ProMP: Proximal Meta-Policy Search. ICLR (Poster) (2019)
Jonas Rothfuss, Fábio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution. IEEE Robotics Autom. Lett. 3 (4) (2018)
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRR (2018)
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel
ProMP: Proximal Meta-Policy Search. CoRR (2018)
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRL (2018)
Fábio Ferreira, Jonas Rothfuss, Eren Erdal Aksoy, You Zhou, Tamim Asfour
Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets. CoRR (2018)
Jonas Rothfuss, Fábio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution. CoRR (2018)