Pascal Klink

According to our database, Pascal Klink authored at least 17 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

On the Benefit of Optimal Transport for Curriculum Reinforcement Learning.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

IEEE Trans. Pattern Anal. Mach. Intell., November, 2024

Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics.

Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters

IEEE Trans. Pattern Anal. Mach. Intell., April, 2024

Domain Randomization via Entropy Maximization.

Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Reinforcement Learning Curricula as Interpolations between Task Distributions.

Pascal Klink

PhD thesis, 2023

Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning.

Pascal Klink, Florian Wolf, Kai Ploeger, Jan Peters, Joni Pajarinen

CoRR, 2023

Function-Space Regularization for Deep Bayesian Classification.

Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters

CoRR, 2023

Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning.

Tobias Niehues, Ulla Scheler, Pascal Klink

CoRR, 2023

2022

Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics.

Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters

CoRR, 2022

Curriculum Reinforcement Learning via Constrained Optimal Transport.

Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the International Conference on Machine Learning, 2022

Boosted Curriculum Reinforcement Learning.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning.

Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen

J. Mach. Learn. Res., 2021

Reinforcement Learning using Guided Observability.

Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen

CoRR, 2021

A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning.

Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Latent Derivative Bayesian Last Layer Networks.

Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Self-Paced Deep Reinforcement Learning.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

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

Generalized Mean Estimation in Monte-Carlo Tree Search.

Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019

Self-Paced Contextual Reinforcement Learning.

Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters

Proceedings of the 3rd Annual Conference on Robot Learning, 2019