Detecting Novelties with Empty Classes
S. Uhlemeyer, J. Lienen, E. Hüllermeier, H. Gottschalk, ArXiv:2305.00983 (2023).

Memorization-Dilation: Modeling Neural Collapse Under Noise
D.A. Nguyen, R. Levie, J. Lienen, G. Kutyniok, E. Hüllermeier, in: International Conference on Learning Representations, ICLR, 2023.

Mitigating Label Noise through Data Ambiguation
J. Lienen, E. Hüllermeier, ArXiv:2305.13764 (2023).

Scikit-Weak: A Python Library for Weakly Supervised Machine Learning
A. Campagner, J. Lienen, E. Hüllermeier, D. Ciucci, in: Lecture Notes in Computer Science, Springer, 2022, pp. 57–70.

Kronecker Decomposition for Knowledge Graph Embeddings
C. Demir, J. Lienen, A.-C. Ngonga Ngomo, ArXiv:2205.06560 (2022).

Conformal Credal Self-Supervised Learning
J. Lienen, C. Demir, E. Hüllermeier, ArXiv:2205.15239 (2022).

Credal Self-Supervised Learning
J. Lienen, E. Hüllermeier, in: Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems, NeurIPS, 2021.

Robust Regression for Monocular Depth Estimation
J. Lienen, N. Nommensen, R. Ewerth, E. Hüllermeier, in: 13th Asian Conference on Machine Learning, ACML, 2021.

Instance weighting through data imprecisiation
J. Lienen, E. Hüllermeier, International Journal of Approximate Reasoning (2021).

From Label Smoothing to Label Relaxation
J. Lienen, E. Hüllermeier, in: Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI, AAAI Press, 2021, pp. 8583–8591.

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

Credal Self-Supervised Learning
J. Lienen, E. Hüllermeier, ArXiv:2106.11853 (2021).

Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model
J. Lienen, E. Hüllermeier, ArXiv:2010.13118 (2020).

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