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Jonas Rothfuss
Jonas Rothfuss
Research Scientist @ Google DeepMind
Bestätigte E-Mail-Adresse bei google.com
Titel
Zitiert von
Zitiert von
Jahr
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
2832018
ProMP: Proximal Meta-Policy Search
J Rothfuss, D Lee, I Clavera, T Asfour, P Abbeel
International Conference on Learning Representations (ICLR) 2019, 2019
2302019
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
J Rothfuss, V Fortuin, M Josifoski, A Krause
International Conference on Machine Learning (ICML) 2021, 2021
1182021
Conditional density estimation with neural networks: Best practices and benchmarks
J Rothfuss, F Ferreira, S Walther, M Ulrich
arXiv preprint arXiv:1903.00954, 2019
772019
DiBS: Differentiable Bayesian Structure Learning
L Lorch, J Rothfuss, B Schölkopf, A Krause
Advances in Neural Information Processing Systems (NeurIPS), 2021
712021
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
53*2021
Amortized Inference for Causal Structure Learning
L Lorch, S Sussex, J Rothfuss, A Krause, B Schölkopf
Advances in Neural Information Processing (NeurIPS), 2022
412022
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
382018
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
342021
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
282019
Meta-Learning Priors for Safe Bayesian Optimization
J Rothfuss, C Koenig, A Rupenyan, A Krause
Conference on Robot Learning (CoRL) 2022, 2022
262022
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice
J Rothfuss, M Josifoski, V Fortuin, A Krause
Journal of Machine Learning Research (JMLR), 2023
142023
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
A Hägele, J Rothfuss, L Lorch, VR Somnath, B Schölkopf, A Krause
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
122023
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
112021
Hallucinated Adversarial Control for Conservative Offline Policy Evaluation
J Rothfuss, B Sukhija, T Birchler, P Kassraie, A Krause
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
92023
Instance-dependent generalization bounds via optimal transport
S Hou, P Kassraie, A Kratsios, J Rothfuss, A Krause
Journal of Machine Learning Reasearch (JMLR), 2023
92023
Meta-Learning Hypothesis Spaces for Sequential Decision-making
P Kassraie, J Rothfuss, A Krause
International Conference on Machine Learning (ICML), 2022
92022
Lifelong Bandit Optimization: No Prior and No Regret
F Schur, P Kassraie, J Rothfuss, A Krause
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
52023
MARS: Meta-learning as score matching in the function space
KL Pavasovic, J Rothfuss, A Krause
International Conference on Learning Representations (ICLR), 2023
52023
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
IEEE Robotics and Automation Letters, 2023
22023
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