| Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning K Schweighofer, M Hofmarcher, MC Dinu, P Renz, A Bitto-Nemling, ... arXiv preprint arXiv:2111.04714, 2021 | 20* | 2021 |
| A dataset perspective on offline reinforcement learning K Schweighofer, M Dinu, A Radler, M Hofmarcher, VP Patil, ... Conference on Lifelong Learning Agents, 470-517, 2022 | 10 | 2022 |
| Addressing Cold Start With Dataset Transfer In E-Commerce Learning To Rank P Missault, A de Myttenaere, A Radler, PA Sondag | 3 | 2021 |
| Toward semantic history compression for reinforcement learning F Paischer, T Adler, A Radler, M Hofmarcher, S Hochreiter Second Workshop on Language and Reinforcement Learning, 2022 | 2 | 2022 |
| Geometry-Informed Neural Networks A Berzins, A Radler, S Sanokowski, S Hochreiter, J Brandstetter arXiv preprint arXiv:2402.14009, 2024 | 1 | 2024 |
| Foundation models for history compression in reinforcement learning F Paischer, T Adler, A Radler, M Hofmarcher, S Hochreiter NeurIPS 2022 Foundation Models for Decision Making Workshop, 2022 | 1 | 2022 |
| Competition Priors for Object-Centric Learning VP Patil, A Radler, D Klotz, S Hochreiter | | |
