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Kajetan Schweighofer
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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
172021
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
72022
Introducing an improved information-theoretic measure of predictive uncertainty
K Schweighofer, L Aichberger, M Ielanskyi, S Hochreiter
arXiv preprint arXiv:2311.08309, 2023
42023
Quantification of Uncertainty with Adversarial Models
K Schweighofer, L Aichberger, M Ielanskyi, G Klambauer, S Hochreiter
Advances in Neural Information Processing Systems 36, 19446-19484, 2023
12023
InfODist: Online distillation with Informative rewards improves generalization in Curriculum Learning
R Siripurapu, VP Patil, K Schweighofer, MC Dinu, T Schmied, LEF Diez, ...
Deep Reinforcement Learning Workshop NeurIPS 2022, 2022
12022
Towards Fully Automated Characterisation of self-assembled Quantum Dots/submitted by Kajetan Schweighofer, MSc
K Schweighofer
2023
The Role of Dataset Generation in Offline Reinforcement Learning/submitted by Kajetan Schweighofer, BSc
K Schweighofer
2021
How many Opinions does your LLM have? Improving Uncertainty Estimation in NLG
L Aichberger, K Schweighofer, M Ielanskyi, S Hochreiter
ICLR 2024 Workshop on Secure and Trustworthy Large Language Models, 0
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Articles 1–8