Self-Paced Contextual Reinforcement Learning. - Pascal Klink; Hany Abdulsamad; Boris Belousov; Jan Peters 001 (CoRL, 2019)

Generalized Mean Estimation in Monte-Carlo Tree Search. - Tuan Dam; Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (IJCAI, 2020)

Self-Paced Deep Reinforcement Learning. - Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (NeurIPS, 2020)

Latent Derivative Bayesian Last Layer Networks. - Joe Watson; Jihao Andreas Lin; Pascal Klink; Joni Pajarinen; Jan Peters 001 (AISTATS, 2021)

A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. - Hany Abdulsamad; Peter Nickl; Pascal Klink; Jan Peters 001 (ICRA, 2021)

Reinforcement Learning using Guided Observability. - Stephan Weigand; Pascal Klink; Jan Peters 001; Joni Pajarinen (CoRR, 2021)

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. - Pascal Klink; Hany Abdulsamad; Boris Belousov; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (J. Mach. Learn. Res., 2021)

Boosted Curriculum Reinforcement Learning. - Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (ICLR, 2022)

Curriculum Reinforcement Learning via Constrained Optimal Transport. - Pascal Klink; Haoyi Yang; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (ICML, 2022)

Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics. - Hany Abdulsamad; Peter Nickl; Pascal Klink; Jan Peters 001 (CoRR, 2022)

Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning. - Tobias Niehues; Ulla Scheler; Pascal Klink (CoRR, 2023)

Function-Space Regularization for Deep Bayesian Classification. - Jihao Andreas Lin; Joe Watson; Pascal Klink; Jan Peters 001 (CoRR, 2023)

Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning. - Pascal Klink; Florian Wolf; Kai Ploeger; Jan Peters 001; Joni Pajarinen (CoRR, 2023)

Reinforcement Learning Curricula as Interpolations between Task Distributions. - Pascal Klink (2023)

Domain Randomization via Entropy Maximization. - Gabriele Tiboni; Pascal Klink; Jan Peters 001; Tatiana Tommasi; Carlo D'Eramo; Georgia Chalvatzaki (ICLR, 2024)

Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics. - Hany Abdulsamad; Peter Nickl; Pascal Klink; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

On the Benefit of Optimal Transport for Curriculum Reinforcement Learning. - Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (IEEE Trans. Pattern Anal. Mach. Intell., 2024)