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Jonas Rothfuss
Jonas Rothfuss
PhD Student, ETH Zurich
Verified email at inf.ethz.ch
Title
Cited by
Cited by
Year
Model-based reinforcement learning via meta-policy optimization
I Clavera, J Rothfuss, J Schulman, Y Fujita, T Asfour, P Abbeel
Conference on Robot Learning (CoRL) 2018, 2018
2062018
ProMP: Proximal Meta-Policy Search
J Rothfuss, D Lee, I Clavera, T Asfour, P Abbeel
International Conference on Learning Representations (ICLR) 2019, 2019
1682019
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
J Rothfuss, V Fortuin, M Josifoski, A Krause
International Conference on Machine Learning (ICML) 2021, 2021
602021
Conditional density estimation with neural networks: Best practices and benchmarks
J Rothfuss, F Ferreira, S Walther, M Ulrich
arXiv preprint arXiv:1903.00954, 2019
432019
Meta-Learning Reliable Priors in the Function Space
J Rothfuss, D Heyn, J Chen, A Krause
Advances in Neural Information Processing Systems 34 (NeurIPS), 2021
27*2021
Deep episodic memory: Encoding, recalling, and predicting episodic experiences for robot action execution
J Rothfuss, F Ferreira, EE Aksoy, Y Zhou, T Asfour
IEEE Robotics and Automation Letters 3 (4), 4007-4014, 2018
242018
Noise regularization for conditional density estimation
J Rothfuss, F Ferreira, S Boehm, S Walther, M Ulrich, T Asfour, A Krause
arXiv preprint arXiv:1907.08982, 2019
172019
DiBS: Differentiable Bayesian Structure Learning
L Lorch, J Rothfuss, B Schölkopf, A Krause
Advances in Neural Information Processing Systems (NeurIPS), 2021
122021
Variational causal networks: Approximate bayesian inference over causal structures
Y Annadani, J Rothfuss, A Lacoste, N Scherrer, A Goyal, Y Bengio, ...
arXiv preprint arXiv:2106.07635, 2021
112021
Robustness to Pruning Predicts Generalization in Deep Neural Networks
L Kuhn, C Lyle, AN Gomez, J Rothfuss, Y Gal
arXiv preprint arXiv:2103.06002, 2021
52021
Meta-Learning Priors for Safe Bayesian Optimization
J Rothfuss, C Koenig, A Rupenyan, A Krause
Conference on Robot Learning (CoRL) 2022, 2022
32022
Meta-Learning Hypothesis Spaces for Sequential Decision-making
P Kassraie, J Rothfuss, A Krause
International Conference on Machine Learning (ICML), 2022
32022
Forward-looking P
M Ulrich, S Walther, J Rothfuss, F Ferreira
22019
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
A Hägele, J Rothfuss, L Lorch, VR Somnath, B Schölkopf, A Krause
arXiv preprint arXiv:2206.01665, 2022
12022
Amortized Inference for Causal Structure Learning
L Lorch, S Sussex, J Rothfuss, A Krause, B Schölkopf
Advances in Neural Information Processing (NeurIPS), 2022
12022
Model-Based Reinforcement Learning via Meta-Policy Optimization
J Rothfuss, I Clavera, J Schulman, T Asfour, P Abbeel
arXiv preprint arXiv:1809.05214, 2018
12018
PAC-Bayesian Meta-Learning: From Theory to Practice
J Rothfuss, M Josifoski, V Fortuin, A Krause
arXiv preprint arXiv:2211.07206, 2022
2022
Instance-Dependent Generalization Bounds via Optimal Transport
S Hou, P Kassraie, A Kratsios, J Rothfuss, A Krause
arXiv preprint arXiv:2211.01258, 2022
2022
Lifelong Bandit Optimization: No Prior and No Regret
F Schur, P Kassraie, J Rothfuss, A Krause
arXiv preprint arXiv:2210.15513, 2022
2022
MARS: Meta-Learning as Score Matching in the Function Space
KL Pavasovic, J Rothfuss, A Krause
arXiv preprint arXiv:2210.13319, 2022
2022
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