Felix Berkenkamp

CoRR, 2024

Information-Theoretic Safe Bayesian Optimization.

[BibT_eX]

[DOI]

CoRR, 2024

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Scalable Meta-Learning with Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics.

[BibT_eX]

[DOI]

Mach. Learn., October, 2023

Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation.

[BibT_eX]

[DOI]

Melrose Roderick

Fatemeh Sheikholeslami

Zico Kolter

CoRR, 2023

Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization.

[BibT_eX]

[DOI]

CoRR, 2023

Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Value-Distributional Model-Based Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Model-Based Uncertainty in Value Functions.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Information-Theoretic Safe Exploration with Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On-Policy Model Errors in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Transfer Learning with Gaussian Processes for Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2020

Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning.

[BibT_eX]

[DOI]

Noah Y. Siegel

Jost Tobias Springenberg

CoRR, 2020

Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning.

[BibT_eX]

[DOI]

Sebastian Curi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Structured Variational Inference in Partially Observable UnstableGaussian Process State Space Models.

[BibT_eX]

[DOI]

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning.

[BibT_eX]

[DOI]

Noah Y. Siegel

Jost Tobias Springenberg

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

No-Regret Bayesian Optimization with Unknown Hyperparameters.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

Structured Variational Inference in Unstable Gaussian Process State Space Models.

[BibT_eX]

[DOI]

CoRR, 2019

Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2019

Safe Exploration for Interactive Machine Learning.

[BibT_eX]

[DOI]

Matteo Turchetta

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Information-Directed Exploration for Deep Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy.

[BibT_eX]

[DOI]

Proceedings of the 17th European Control Conference, 2019

2018

The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamic Systems.

[BibT_eX]

[DOI]

Spencer M. Richards

CoRR, 2018

Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2018

Verifying Controllers Against Adversarial Examples with Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems.

[BibT_eX]

[DOI]

Spencer M. Richards

Proceedings of the 2nd Annual Conference on Robot Learning, 2018

Learning-Based Model Predictive Control for Safe Exploration.

[BibT_eX]

[DOI]

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Safe Model-based Reinforcement Learning with Stability Guarantees.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

2016

Safe Exploration in Finite Markov Decision Processes with Gaussian Processes.

[BibT_eX]

[DOI]

Matteo Turchetta

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Safe controller optimization for quadrotors with Gaussian processes.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

Bayesian optimization for maximum power point tracking in photovoltaic power plants.

[BibT_eX]

[DOI]

Proceedings of the 15th European Control Conference, 2016

Safe learning of regions of attraction for uncertain, nonlinear systems with Gaussian processes.

[BibT_eX]

[DOI]

Proceedings of the 55th IEEE Conference on Decision and Control, 2016

2015

Safe and robust learning control with Gaussian processes.

[BibT_eX]

[DOI]