Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. - Julian Lienen; Eyke Hüllermeier (CoRR, 2020)

From Label Smoothing to Label Relaxation. - Julian Lienen; Eyke Hüllermeier (AAAI, 2021)

Robust Regression for Monocular Depth Estimation. - Julian Lienen; Nils Nommensen; Ralph Ewerth; Eyke Hüllermeier (ACML, 2021)

Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model. - Julian Lienen; Eyke Hüllermeier; Ralph Ewerth; Nils Nommensen (CVPR, 2021)

Credal Self-Supervised Learning. - Julian Lienen; Eyke Hüllermeier (NeurIPS, 2021)

Instance weighting through data imprecisiation. - Julian Lienen; Eyke Hüllermeier (Int. J. Approx. Reason., 2021)

Kronecker Decomposition for Knowledge Graph Embeddings. - Caglar Demir; Julian Lienen; Axel-Cyrille Ngonga Ngomo (HT, 2022)

Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. - Andrea Campagner; Julian Lienen; Eyke Hüllermeier; Davide Ciucci (IJCRS, 2022)

Conformal Credal Self-Supervised Learning. - Julian Lienen; Caglar Demir; Eyke Hüllermeier (COPA, 2023)

Memorization-Dilation: Modeling Neural Collapse Under Noise. - Duc Anh Nguyen; Ron Levie; Julian Lienen; Eyke Hüllermeier; Gitta Kutyniok (ICLR, 2023)

Detecting Novelties with Empty Classes. - Svenja Uhlemeyer; Julian Lienen; Eyke Hüllermeier; Hanno Gottschalk (CoRR, 2023)

Robustifying machine learning through weakening supervision. - Julian Lienen (2023)

Mitigating Label Noise through Data Ambiguation. - Julian Lienen; Eyke Hüllermeier (AAAI, 2024)

Unsupervised Class Incremental Learning using Empty Classes. - Svenja Uhlemeyer; Julian Lienen; Youssef Shoeb; Eyke Hüllermeier; Hanno Gottschalk (BMVC Workshops, 2024)