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ORCIDKajetan Schweighofer
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2020 – today
- 2025
[j1]Bola Khalil
, Kajetan Schweighofer, Natalia Dyubankova, Gerard J. P. van Westen, Herman van Vlijmen:
Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling. J. Chem. Inf. Model. 65(24): 13057-13069 (2025)- [c5]Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi, Sepp Hochreiter:
Improving Uncertainty Estimation through Semantically Diverse Language Generation. ICLR 2025 - [c4]Kajetan Schweighofer, Adrián Arnaiz-Rodríguez, Sepp Hochreiter, Nuria Oliver:
The Disparate Benefits of Deep Ensembles. ICML 2025 - [c3]Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter:
On Information-Theoretic Measures of Predictive Uncertainty. UAI 2025: 3605-3640 - [i12]Piera Riccio, Francesco Galati, Kajetan Schweighofer, Noa Garcia, Nuria Oliver:
ImageSet2Text: Describing Sets of Images through Text. CoRR abs/2503.19361 (2025) - [i11]Kajetan Schweighofer, Barbara Brune, Lukas Gruber, Simon Schmid, Alexander Aufreiter, Andreas Gruber, Thomas Doms, Sebastian Eder, Florian Mayer, Xaver-Paul Stadlbauer, Christoph Schwald, Werner Zellinger, Bernhard Nessler, Sepp Hochreiter:
Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned. CoRR abs/2509.08852 (2025) - [i10]Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi, Nikita Kotelevskii, Mohsen Guizani, Maxim Panov:
Uncertainty Quantification for Regression using Proper Scoring Rules. CoRR abs/2509.26610 (2025) - [i9]Maximilian Beck, Kajetan Schweighofer, Sebastian Böck, Sebastian Lehner, Sepp Hochreiter:
xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity. CoRR abs/2510.02228 (2025) - [i8]Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger, Sepp Hochreiter:
Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation. CoRR abs/2510.02279 (2025) - 2024
- [i7]Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi, Sepp Hochreiter:
Semantically Diverse Language Generation for Uncertainty Estimation in Language Models. CoRR abs/2406.04306 (2024) - [i6]Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter:
On Information-Theoretic Measures of Predictive Uncertainty. CoRR abs/2410.10786 (2024) - [i5]Kajetan Schweighofer, Adrián Arnaiz-Rodríguez, Sepp Hochreiter, Nuria Oliver:
The Disparate Benefits of Deep Ensembles. CoRR abs/2410.13831 (2024) - [i4]Lukas Aichberger, Kajetan Schweighofer, Sepp Hochreiter:
Rethinking Uncertainty Estimation in Natural Language Generation. CoRR abs/2412.15176 (2024) - 2023
- [c2]Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Günter Klambauer, Sepp Hochreiter:
Quantification of Uncertainty with Adversarial Models. NeurIPS 2023 - [i3]Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Günter Klambauer, Sepp Hochreiter:
Quantification of Uncertainty with Adversarial Models. CoRR abs/2307.03217 (2023) - [i2]Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter:
Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty. CoRR abs/2311.08309 (2023) - 2022
- [c1]Kajetan Schweighofer, Marius-Constantin Dinu, Andreas Radler, Markus Hofmarcher, Vihang Prakash Patil, Angela Bitto-Nemling, Hamid Eghbal-zadeh, Sepp Hochreiter:
A Dataset Perspective on Offline Reinforcement Learning. CoLLAs 2022: 470-517 - 2021
- [i1]Kajetan Schweighofer, Markus Hofmarcher, Marius-Constantin Dinu, Philipp Renz, Angela Bitto-Nemling, Vihang Patil, Sepp Hochreiter:
Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning. CoRR abs/2111.04714 (2021)
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