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Pascal Klink
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2020 – today
- 2024
- [j3]Hany Abdulsamad
, Peter Nickl
Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics. IEEE Trans. Pattern Anal. Mach. Intell. 46(4): 1950-1963 (2024) - [j2]Pascal Klink
On the Benefit of Optimal Transport for Curriculum Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(11): 7191-7204 (2024) - [c8]Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki:
Domain Randomization via Entropy Maximization. ICLR 2024 - 2023
- [b1]Pascal Klink:
Reinforcement Learning Curricula as Interpolations between Task Distributions. Technical University of Darmstadt, Germany, 2023 - [i12]Tobias Niehues, Ulla Scheler, Pascal Klink:
Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning. CoRR abs/2306.05769 (2023) - [i11]Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters:
Function-Space Regularization for Deep Bayesian Classification. CoRR abs/2307.06055 (2023) - [i10]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
On the Benefit of Optimal Transport for Curriculum Reinforcement Learning. CoRR abs/2309.14091 (2023) - [i9]Pascal Klink, Florian Wolf, Kai Ploeger, Jan Peters, Joni Pajarinen:
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning. CoRR abs/2309.14096 (2023) - [i8]Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki:
Domain Randomization via Entropy Maximization. CoRR abs/2311.01885 (2023) - 2022
- [c7]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Boosted Curriculum Reinforcement Learning. ICLR 2022 - [c6]Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Curriculum Reinforcement Learning via Constrained Optimal Transport. ICML 2022: 11341-11358 - [i7]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics. CoRR abs/2211.01120 (2022) - 2021
- [j1]Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. J. Mach. Learn. Res. 22: 182:1-182:52 (2021) - [c5]Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters:
Latent Derivative Bayesian Last Layer Networks. AISTATS 2021: 1198-1206 - [c4]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. ICRA 2021: 4216-4222 - [i6]Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. CoRR abs/2102.13176 (2021) - [i5]Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen:
Reinforcement Learning using Guided Observability. CoRR abs/2104.10986 (2021) - 2020
- [c3]Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Generalized Mean Estimation in Monte-Carlo Tree Search. IJCAI 2020: 2397-2404 - [c2]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Self-Paced Deep Reinforcement Learning. NeurIPS 2020 - [i4]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Self-Paced Deep Reinforcement Learning. CoRR abs/2004.11812 (2020) - [i3]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. CoRR abs/2011.05217 (2020)
2010 – 2019
- 2019
- [c1]Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters:
Self-Paced Contextual Reinforcement Learning. CoRL 2019: 513-529 - [i2]Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters:
Self-Paced Contextual Reinforcement Learning. CoRR abs/1910.02826 (2019) - [i1]Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Generalized Mean Estimation in Monte-Carlo Tree Search. CoRR abs/1911.00384 (2019)
Coauthor Index
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last updated on 2024-11-07 20:24 CET by the dblp team
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