1. 2018
  2. Supervised Classification: Quite a Brief Overview

    Loog, M., 2018, Machine Learning Techniques for Space Weather. Camporeale, E., Wing, S. & Johnson, J. R. (eds.). Elsevier, p. 113-145 33 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

  3. Template Matching via Densities on the Roto-Translation Group

    Bekkers, E. J., Loog, M., ter Haar Romeny, B. M. & Duits, R., 2018, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 40, 2, p. 452-466 15 p., 7864477.

    Research output: Contribution to journalArticleScientificpeer-review

  4. The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning

    Krijthe, J. H. & Loog, M., 2018, NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Bengio, S., Wallach, H. M., Larochelle, H., Grauman, K. & Cesa-Bianchi, N. (eds.). Curran Associates, Inc., p. 1793-1802 10 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  5. Towards Practical Active Learning for Classification

    Yang, Y., 2018, 133 p.

    Research output: ThesisDissertation (TU Delft)

  6. 2019
  7. A dissimilarity-based multiple instance learning approach for protein remote homology detection

    Mensi, A., Bicego, M., Lovato, P., Loog, M. & Tax, D. M. J., 2019, In : Pattern Recognition Letters. 128, p. 231-236 6 p.

    Research output: Contribution to journalArticleScientificpeer-review

  8. Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations

    Tian, X., ten Veldhuis, M. C., Schleiss, M., Bouwens, C. & van de Giesen, N., 2019, In : Science of the Total Environment. 689, p. 258-268 11 p.

    Research output: Contribution to journalArticleScientificpeer-review

  9. Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling

    Venkatraghavan, V., Bron, E. E., Niessen, W. J. & Klein, S., 2019, In : NeuroImage. 186, p. 518-532

    Research output: Contribution to journalArticleScientificpeer-review

  10. Electrochemical recycling of rare earth elements from NdFeB magnet waste

    Venkatesan, P., 2019, 90 p.

    Research output: ThesisDissertation (TU Delft)

  11. Gaussian process variance reduction by location selection

    Bottarelli, L. & Loog, M., 2019, In : Pattern Recognition Letters. 125, p. 727-734 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

  12. Learning an MR acquisition-invariant representation using Siamese neural networks

    Kouw, W. M., Loog, M., Bartels, L. W. & Mendrik, A. M., 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) : Proceedings. Danvers: IEEE, p. 364-367 4 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  13. On the Statistical Detection of Adversarial Instances over Encrypted Data

    Sheikhalishahi, M., Nateghizad, M., Martinelli, F., Erkin, Z. & Loog, M., 2019, Security and Trust Management - 15th International Workshop, STM 2019, Proceedings. Mauw, S. & Conti, M. (eds.). Springer, p. 71-88 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11738 LNCS).

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  14. PRECISE: A domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors

    Mourragui, S., Loog, M., van der Wiel, M. A., Reinders, M. & Wessels, L., 2019, In : Bioinformatics. 35, 14, p. i510-i519 10 p., btz372.

    Research output: Contribution to journalArticleScientificpeer-review

  15. Robust Importance-Weighted Cross-Validation under Sample Selection Bias

    Kouw, W. M., Krijthe, J. H. & Loog, M., 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). Piscataway: IEEE, p. 1-6 6 p. 8918731

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  16. Single shot active learning using pseudo annotators

    Yang, Y. & Loog, M., 2019, In : Pattern Recognition. 89, p. 22-31 10 p.

    Research output: Contribution to journalArticleScientificpeer-review

  17. Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh’s drawings

    Zeng, Y., van der Lubbe, J. C. A. & Loog, M., 1 Oct 2019, In : Machine Vision and Applications. 30, 7-8, p. 1229-1241 13 p.

    Research output: Contribution to journalArticleScientificpeer-review

  18. 2020
  19. A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

    Mey, A., Viering, T. J. & Loog, M., 2020, Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings. Berthold, M. R., Feelders, A. & Krempl, G. (eds.). Springer Open, p. 326-338 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12080 LNCS).

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  20. Assumptions & Expectations in Semi-Supervised Machine Learning

    Mey, A., 2020, 117 p.

    Research output: ThesisDissertation (TU Delft)

  21. Making Learners (More) Monotone

    Viering, T. J., Mey, A. & Loog, M., 2020, Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings. Berthold, M. R., Feelders, A. & Krempl, G. (eds.). Cham: Springer Open, p. 535-547 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12080 ).

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  22. Semi-generative modelling: Covariate-shift adaptation with cause and effect features

    von Kügelgen, J., Mey, A. & Loog, M., 2020, In : Proceedings of Machine Learning Research. 89, 9 p.

    Research output: Contribution to journalConference articleScientificpeer-review

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