1. 2020
  2. 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

  3. Assumptions & Expectations in Semi-Supervised Machine Learning

    Mey, A., 2020, 117 p.

    Research output: ThesisDissertation (TU Delft)

  4. 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

  5. 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

  6. 2019
  7. 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

  8. 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

  9. 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

  10. 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

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

    Venkatesan, P., 2019, 90 p.

    Research output: ThesisDissertation (TU Delft)

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 2018
  19. A benchmark and comparison of active learning for logistic regression

    Yang, Y. & Loog, M., 2018, In : Pattern Recognition. 83, p. 401-415 15 p.

    Research output: Contribution to journalArticleScientificpeer-review

  20. A spatio-temporal reference model of the aging brain

    huizinga, W., Poot, D., Vernooij, M. W., Roshchupkin, G. V., bron, E. E., Ikram, M. A., Rueckert, D., Niessen, W. & Klein, S., 2018, In : NeuroImage. 169, p. 11-22

    Research output: Contribution to journalArticleScientificpeer-review

  21. A variance maximization criterion for active learning

    Yang, Y. & Loog, M., 2018, In : Pattern Recognition. 78, p. 358-370 13 p.

    Research output: Contribution to journalArticleScientificpeer-review

  22. Asymmetric kernel in Gaussian Processes for learning target variance

    Pintea, S. L., van Gemert, J. C. & Smeulders, A. W. M., 2018, In : Pattern Recognition Letters. 108, p. 70-77 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

  23. Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI

    van Stralen, M., Zhou, Y., Wozny, P. J., Seevinck, P. R. & Loog, M., 2018, Medical Imaging 2018: Image Processing. Angelini, E. D. & Landman, B. A. (eds.). Bellingham: SPIE, p. 105741N-1 - 105741N-6 6 p. 105741N. (Proceedings of Spie; vol. 10574).

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

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