Pascal Klink - RankMe

24 papers A* 6A 1Misc 1Journal 15

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	Domain Randomization via Entropy Maximization. ICLR Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki
2024	J	`jnl`	On the Benefit of Optimal Transport for Curriculum Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2024	J	`jnl`	Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics. IEEE Trans. Pattern Anal. Mach. Intell. Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters
2023	J	`jnl`	Domain Randomization via Entropy Maximization. CoRR Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki
2023	J	`jnl`	Function-Space Regularization for Deep Bayesian Classification. CoRR Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters
2023	J	`jnl`	On the Benefit of Optimal Transport for Curriculum Reinforcement Learning. CoRR Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2023	—	`—`	Reinforcement Learning Curricula as Interpolations between Task Distributions. Pascal Klink
2023	J	`jnl`	Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning. CoRR Tobias Niehues, Ulla Scheler, Pascal Klink
2023	J	`jnl`	Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning. CoRR Pascal Klink, Florian Wolf, Kai Ploeger, Jan Peters, Joni Pajarinen
2022	A*	`conf`	Boosted Curriculum Reinforcement Learning. ICLR Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2022	A*	`conf`	Curriculum Reinforcement Learning via Constrained Optimal Transport. ICML Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2022	J	`jnl`	Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics. CoRR Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters
2021	J	`jnl`	A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. CoRR Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2021	J	`jnl`	A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. J. Mach. Learn. Res. Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2021	A*	`conf`	A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. ICRA Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters
2021	A	`conf`	Latent Derivative Bayesian Last Layer Networks. AISTATS Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters
2021	J	`jnl`	Reinforcement Learning using Guided Observability. CoRR Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen
2020	J	`jnl`	A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. CoRR Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters
2020	A*	`conf`	Generalized Mean Estimation in Monte-Carlo Tree Search. IJCAI Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2020	A*	`conf`	Self-Paced Deep Reinforcement Learning. NeurIPS Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2020	J	`jnl`	Self-Paced Deep Reinforcement Learning. CoRR Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2019	J	`jnl`	Generalized Mean Estimation in Monte-Carlo Tree Search. CoRR Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
2019	Misc	`conf`	Self-Paced Contextual Reinforcement Learning. CoRL Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters
2019	J	`jnl`	Self-Paced Contextual Reinforcement Learning. CoRR Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters