Uhlemeyer, Svenja, Julian Lienen, Eyke Hüllermeier, and Hanno Gottschalk. “Detecting Novelties with Empty Classes.” ArXiv:2305.00983, 2023.
LibreCat: /record/44512
arXiv: 2305.00983

Nguyen, Duc Anh, Ron Levie, Julian Lienen, Gitta Kutyniok, and Eyke Hüllermeier. “Memorization-Dilation: Modeling Neural Collapse Under Noise.” In International Conference on Learning Representations, ICLR, 2023.
LibreCat: /record/31880

Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data Ambiguation.” ArXiv:2305.13764, 2023.
LibreCat: /record/45911
arXiv: 2305.13764

Campagner, Andrea, Julian Lienen, Eyke Hüllermeier, and Davide Ciucci. “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.” In Lecture Notes in Computer Science, 13633:57–70. Springer, 2022.
LibreCat: /record/34542

Demir, Caglar, Julian Lienen, and Axel-Cyrille Ngonga Ngomo. “Kronecker Decomposition for Knowledge Graph Embeddings.” ArXiv:2205.06560, 2022.
LibreCat: /record/31545

Lienen, Julian, Caglar Demir, and Eyke Hüllermeier. “Conformal Credal Self-Supervised Learning.” ArXiv:2205.15239, 2022.
LibreCat: /record/31546

Lienen, Julian, and Eyke Hüllermeier. “Credal Self-Supervised Learning.” In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems, NeurIPS, 2021.
LibreCat: /record/27161

Lienen, Julian, Nils Nommensen, Ralph Ewerth, and Eyke Hüllermeier. “Robust Regression for Monocular Depth Estimation.” In 13th Asian Conference on Machine Learning, ACML, 2021.
LibreCat: /record/27162

Lienen, Julian, and Eyke Hüllermeier. “Instance Weighting through Data Imprecisiation.” International Journal of Approximate Reasoning, 2021.
LibreCat: /record/21636

Lienen, Julian, and Eyke Hüllermeier. “From Label Smoothing to Label Relaxation.” In Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI, 35:8583–91. AAAI Press, 2021.
LibreCat: /record/21637

Lienen, Julian, Eyke Hüllermeier, Ralph Ewerth, and Nils 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, 14595–604, 2021.
LibreCat: /record/22280

Lienen, Julian, and Eyke Hüllermeier. “Credal Self-Supervised Learning.” ArXiv:2106.11853, 2021.
LibreCat: /record/22509

Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce  Model.” ArXiv:2010.13118, 2020.
LibreCat: /record/20211

Lienen, Julian. Automated Feature Engineering on Time Series Data, 2019.
LibreCat: /record/16415