Journal of Machine Learning Research

Research outputs

  1. 2018
  2. Published

    ELFI: Engine for likelihood-free inference

    Lintusaari, J., Vuollekoski, H., Kangasrääsiö, A., Skytén, K., Järvenpää, M., Marttinen, P., Gutmann, M. U., Vehtari, A., Corander, J. & Kaski, S., 1 Aug 2018, In : Journal of Machine Learning Research. 19, p. 1-7 7 p.

    Research output: Contribution to journalArticleScientificpeer-review

  3. Published

    Variational Fourier Features for Gaussian Processes

    Hensman, J., Durrande, N. & Solin, A., 2018, In : Journal of Machine Learning Research. 18, 1, p. 1-52 52 p., 151.

    Research output: Contribution to journalArticleScientificpeer-review

  4. 2017
  5. Published

    Bayesian inference for spatio-temporal spike-and-slab priors

    Andersen, M. R., Vehtari, A., Winther, O. & Kai Hansen, L., 1 Dec 2017, In : Journal of Machine Learning Research. 18, p. 1-58

    Research output: Contribution to journalArticleScientificpeer-review

  6. Published

    GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis

    Leppäaho, E., Ammad-ud-din, M. & Kaski, S., 2017, In : Journal of Machine Learning Research. 18, p. 1-5 5 p., 39.

    Research output: Contribution to journalArticleScientificpeer-review

  7. 2016
  8. Published

    Low-rank doubly stochastic matrix decomposition for cluster analysis

    Yang, Z., Corander, J. & Oja, E., 1 Oct 2016, In : Journal of Machine Learning Research. 17, 25 p.

    Research output: Contribution to journalArticleScientificpeer-review

  9. Published

    Bayesian optimization for likelihood-free inference of simulator-based statistical models

    Gutmann, M. U. & Corander, J., 1 Aug 2016, In : Journal of Machine Learning Research. 17, p. 1-47 47 p.

    Research output: Contribution to journalReview ArticleScientificpeer-review

  10. Published

    Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models

    Vehtari, A., Mononen, T., Tolvanen, V., Sivula, T. & Winther, O., 1 Jun 2016, In : Journal of Machine Learning Research. 17, p. 1-38 38 p.

    Research output: Contribution to journalArticleScientificpeer-review

  11. Published

    Multiple output regression with latent noise

    Gillberg, L., Marttinen, P., Pirinen, M., Kangas, A. J., Soininen, P., Ali, M., Havulinna, A. S., Järvelin, M. R., Ala-Korpela, M. & Kaski, S., 1 Jun 2016, In : Journal of Machine Learning Research. 17, p. 1-35 35 p.

    Research output: Contribution to journalArticleScientificpeer-review

  12. Published

    BayesPy: Variational Bayesian inference in Python

    Luttinen, J., 1 Apr 2016, In : Journal of Machine Learning Research. 17, p. 1-6 41.

    Research output: Contribution to journalArticleScientificpeer-review

  13. Published

    Structure discovery in Bayesian networks by sampling partial orders

    Niinimäki, T., Parviainen, P. & Koivisto, M., 1 Apr 2016, In : Journal of Machine Learning Research. 17

    Research output: Contribution to journalArticleScientificpeer-review

  14. Published

    MEKA: A multi-label/multi-target extension to WEKA

    Read, J., Reutemann, P., Pfahringer, B. & Holmes, G., 1 Feb 2016, In : Journal of Machine Learning Research. 17, p. 1-5 21.

    Research output: Contribution to journalArticleScientificpeer-review

  15. Published

    Input Output Kernel Regression: supervised and semi-supervised structured output prediction with operator-valued kernels

    Brouard, C., Szafranski, M. & d'Alché-Buc, F., 2016, In : Journal of Machine Learning Research. 17, 176, p. 1-48 48 p.

    Research output: Contribution to journalArticleScientificpeer-review

  16. Published

    Learning Taxonomy Adaptation in Large-scale Classification

    Babbar, R., Partalas, I., Gaussier, E., Amini, M-R. & Amblard, C., 2016, In : Journal of Machine Learning Research.

    Research output: Contribution to journalArticleScientificpeer-review

  17. 2015
  18. Published

    The algebraic combinatorial approach for Low-Rank Matrix Completion

    Király, F. J., Theran, L. & Tomioka, R., 2015, In : Journal of Machine Learning Research. 16, p. 1391-1436

    Research output: Contribution to journalArticleScientificpeer-review

  19. 2014
  20. Published

    Expectation Propagation for Neural Networks with Sparsity-Promoting Priors

    Jylanki, P., Nummenmaa, A. & Vehtari, A., 2014, In : Journal of Machine Learning Research. 15, May, p. 1849-1901

    Research output: Contribution to journalArticleScientificpeer-review

  21. Published

    Preface

    Kaski, S. & Corander, J., 2014, In : Journal of Machine Learning Research. 33, p. i-iv

    Research output: Contribution to journalArticleScientificpeer-review

  22. 2013
  23. Published

    Bayesian Canonical Correlation Analysis

    Klami, A., Virtanen, S. & Kaski, S., 2013, In : Journal of Machine Learning Research. 14, p. 965-1003

    Research output: Contribution to journalArticleScientificpeer-review

  24. Published

    GPstuff: Bayesian Modeling with Gaussian Processes

    Vanhatalo, J., Riihimäki, J., Hartikainen, J., Jylänki, P., Tolvanen, V. & Vehtari, A., 2013, In : Journal of Machine Learning Research. 14, p. 1175-1179

    Research output: Contribution to journalArticleScientificpeer-review

  25. Published

    Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood

    Riihimäki, J., Jylänki, P. & Vehtari, A., 2013, In : Journal of Machine Learning Research. 14, p. 75-109

    Research output: Contribution to journalArticleScientificpeer-review

  26. 2011
  27. Published

    Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood

    Carvalho, A. M., Roos, T., Oliveira, A. L. & Myllymäki, P., Jul 2011, In : Journal of Machine Learning Research. 12, p. 2181-2210 30 p.

    Research output: Contribution to journalArticleScientificpeer-review

  28. Published

    Robust Gaussian Process Regression with a Student-t Likelihood

    Jylänki, P., Vanhatalo, J. & Vehtari, A., 2011, In : Journal of Machine Learning Research. 12, p. 3227-3257

    Research output: Contribution to journalArticleScientificpeer-review

  29. 2010
  30. Published

    Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes

    Honkela, A., Raiko, T., Kuusela, M., Tornio, M. & Karhunen, J., 2010, In : Journal of Machine Learning Research. 11, 11, p. 3235-3268

    Research output: Contribution to journalArticleScientificpeer-review

  31. Published

    Gaussian processes with monotonicity information

    Riihimäki, J. & Vehtari, A., 2010, In : Journal of Machine Learning Research. AISTATS2010 special issue, p. 645-652

    Research output: Contribution to journalArticleScientificpeer-review

  32. Published

    Information retrieval perspective to nonlinear dimensionality reduction for data visualization

    Venna, J., Peltonen, J., Nybo, K., Aidos, H. & Kaski, S., 2010, In : Journal of Machine Learning Research. 11, 3, p. 451-490

    Research output: Contribution to journalArticleScientificpeer-review

  33. Published

    Permutation Tests for Studying Classifier Performance

    Ojala, M. & Garriga, G., 2010, In : Journal of Machine Learning Research. 11, 3, p. 1833-1863

    Research output: Contribution to journalArticleScientificpeer-review

  34. Published

    Practical Approaches to Principal Component Analysis in the Presence of Missing Values

    Ilin, A. & Raiko, T., 2010, In : Journal of Machine Learning Research. 11, 3, p. 1957-2000

    Research output: Contribution to journalArticleScientificpeer-review

  35. 2008
  36. Published

    Closed Sets for Labeled Data

    Garriga, G. C., Kralj, P. & Lavrac, N., 2008, In : Journal of Machine Learning Research. 9, p. 559-580

    Research output: Contribution to journalArticleScientificpeer-review

  37. 2007
  38. Published

    Building Blocks for Variational Bayesian Learning of Latent Variable Models

    Raiko, T., Valpola, H., Harva, M. & Karhunen, J., 2007, In : Journal of Machine Learning Research. 8, p. 155-201

    Research output: Contribution to journalArticleScientificpeer-review

  39. Published

    Distances between data sets based on summary statistics

    Tatti, N., 2007, In : Journal of Machine Learning Research. 8, p. 131-154

    Research output: Contribution to journalArticleScientificpeer-review

  40. 2005
  41. Published

    Denoising Source Separation

    Särelä, J. & Valpola, H., 2005, In : Journal of Machine Learning Research. 6, p. 233-272

    Research output: Contribution to journalArticleScientificpeer-review

  42. 2003
  43. Published

    Introduction to Special Issue on Independent Component Analysis

    Lee, T-W., Cardoso, J-F. & Oja, E., 2003, In : Journal of Machine Learning Research. 4, Special issue, p. 1175-1176

    Research output: Contribution to journalArticleScientificpeer-review

ID: 306157