Jonas Rothfuss

Orcid: 0000-0003-0129-0540

According to our database1, Jonas Rothfuss authored at least 25 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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2024
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Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Meta-Learning Priors from Limited Data: From Theory to Practice.
PhD thesis, 2024

Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning.
IEEE Robotics Autom. Lett., 2024

Bridging the Sim-to-Real Gap with Bayesian Inference.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024

2023
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice.
J. Mach. Learn. Res., 2023

Instance-Dependent Generalization Bounds via Optimal Transport.
J. Mach. Learn. Res., 2023

Lifelong bandit optimization: no prior and no regret.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Hallucinated adversarial control for conservative offline policy evaluation.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

MARS: Meta-learning as Score Matching in the Function Space.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

BaCaDI: Bayesian Causal Discovery with Unknown Interventions.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
PAC-Bayesian Meta-Learning: From Theory to Practice.
CoRR, 2022

Amortized Inference for Causal Structure Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Meta-Learning Hypothesis Spaces for Sequential Decision-making.
Proceedings of the International Conference on Machine Learning, 2022

Meta-Learning Priors for Safe Bayesian Optimization.
Proceedings of the Conference on Robot Learning, 2022

2021
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures.
CoRR, 2021

Robustness to Pruning Predicts Generalization in Deep Neural Networks.
CoRR, 2021

Meta-Learning Reliable Priors in the Function Space.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

DiBS: Differentiable Bayesian Structure Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees.
CoRR, 2020

2019
Noise Regularization for Conditional Density Estimation.
CoRR, 2019

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks.
CoRR, 2019

ProMP: Proximal Meta-Policy Search.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution.
IEEE Robotics Autom. Lett., 2018

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets.
CoRR, 2018

Model-Based Reinforcement Learning via Meta-Policy Optimization.
Proceedings of the 2nd Annual Conference on Robot Learning, 2018


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