14 Publications
2023 | Preprint | LibreCat-ID: 44512 | 
S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties with Empty Classes,” arXiv:2305.00983. 2023.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 31880 |
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.
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2023 | Preprint | LibreCat-ID: 45911 |
J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,” arXiv:2305.13764. 2023.
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| arXiv
2022 | Conference Paper | LibreCat-ID: 34542
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.
LibreCat
2022 | Preprint | LibreCat-ID: 31545 |
C. Demir, J. Lienen, and A.-C. Ngonga Ngomo, “Kronecker Decomposition for Knowledge Graph Embeddings,” arXiv:2205.06560. 2022.
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2022 | Preprint | LibreCat-ID: 31546 |
J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” arXiv:2205.15239. 2022.
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2021 | Conference Paper | LibreCat-ID: 27161
J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” presented at the Annual Conference on Neural Information Processing Systems, NeurIPS, Online, 2021.
LibreCat
2021 | Conference Paper | LibreCat-ID: 27162
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.
LibreCat
2021 | Journal Article | LibreCat-ID: 21636
J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,” International Journal of Approximate Reasoning, 2021.
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2021 | Conference Paper | LibreCat-ID: 21637 |
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.
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2021 | Conference Paper | LibreCat-ID: 22280
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.
LibreCat
2021 | Preprint | LibreCat-ID: 22509 |
J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” arXiv:2106.11853. 2021.
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2020 | Preprint | LibreCat-ID: 20211 |
J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce model,” arXiv:2010.13118. 2020.
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2019 | Mastersthesis | LibreCat-ID: 16415
J. Lienen, Automated Feature Engineering on Time Series Data. 2019.
LibreCat
14 Publications
2023 | Preprint | LibreCat-ID: 44512 |
S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties with Empty Classes,” arXiv:2305.00983. 2023.
LibreCat
| Download (ext.)
| arXiv
2023 | Conference Paper | LibreCat-ID: 31880 |
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.
LibreCat
| Download (ext.)
2023 | Preprint | LibreCat-ID: 45911 |
J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,” arXiv:2305.13764. 2023.
LibreCat
| Download (ext.)
| arXiv
2022 | Conference Paper | LibreCat-ID: 34542
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.
LibreCat
2022 | Preprint | LibreCat-ID: 31545 |
C. Demir, J. Lienen, and A.-C. Ngonga Ngomo, “Kronecker Decomposition for Knowledge Graph Embeddings,” arXiv:2205.06560. 2022.
LibreCat
| Download (ext.)
2022 | Preprint | LibreCat-ID: 31546 |
J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” arXiv:2205.15239. 2022.
LibreCat
| Download (ext.)
2021 | Conference Paper | LibreCat-ID: 27161
J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” presented at the Annual Conference on Neural Information Processing Systems, NeurIPS, Online, 2021.
LibreCat
2021 | Conference Paper | LibreCat-ID: 27162
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.
LibreCat
2021 | Journal Article | LibreCat-ID: 21636
J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,” International Journal of Approximate Reasoning, 2021.
LibreCat
| Download (ext.)
2021 | Conference Paper | LibreCat-ID: 21637 |
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.
LibreCat
| Download (ext.)
2021 | Conference Paper | LibreCat-ID: 22280
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.
LibreCat
2021 | Preprint | LibreCat-ID: 22509 |
J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” arXiv:2106.11853. 2021.
LibreCat
| Download (ext.)
2020 | Preprint | LibreCat-ID: 20211 |
J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce model,” arXiv:2010.13118. 2020.
LibreCat
| Download (ext.)
2019 | Mastersthesis | LibreCat-ID: 16415
J. Lienen, Automated Feature Engineering on Time Series Data. 2019.
LibreCat