S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties with Empty Classes,” arXiv:2305.00983. 2023.

D. A. Nguyen, R. Levie, J. Lienen, G. Kutyniok, and E. Hüllermeier, “Memorization-Dilation: Modeling Neural Collapse Under Noise,” presented at the International Conference on Learning Representations, ICLR, Kigali, Ruanda, 2023.

J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,” arXiv:2305.13764. 2023.

A. Campagner, J. Lienen, E. Hüllermeier, and D. Ciucci, “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning,” in Lecture Notes in Computer Science, Suzhou, China, 2022, vol. 13633, pp. 57–70.

C. Demir, J. Lienen, and A.-C. Ngonga Ngomo, “Kronecker Decomposition for Knowledge Graph Embeddings,” arXiv:2205.06560. 2022.

J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” arXiv:2205.15239. 2022.

J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” presented at the Annual Conference on Neural Information Processing Systems, NeurIPS, Online, 2021.

J. Lienen, N. Nommensen, R. Ewerth, and E. Hüllermeier, “Robust Regression for Monocular Depth Estimation,” presented at the 13th Asian Conference on Machine Learning, ACML, Online, 2021.

J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,” International Journal of Approximate Reasoning, 2021.

J. Lienen and E. Hüllermeier, “From Label Smoothing to Label Relaxation,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI, Online, 2021, vol. 35, no. 10, pp. 8583–8591.

J. Lienen, E. Hüllermeier, R. Ewerth, and N. Nommensen, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Online, 2021, pp. 14595–14604.

J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” arXiv:2106.11853. 2021.

J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model,” arXiv:2010.13118. 2020.

J. Lienen, Automated Feature Engineering on Time Series Data. 2019.