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 journalArticle

  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 journalArticle

  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 journalArticle

  6. Published

    Magnitude-preserving ranking for structured outputs

    Brouard, C., Bach, E., Böcker, S. & Rousu, J., 1 Jan 2017, In : Journal of Machine Learning Research. 77, p. 407-422 16 p.

    Research output: Contribution to journalConference article

  7. Published
  8. 2016
  9. 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 journalArticle

  10. 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 Article

  11. 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 journalArticle

  12. 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 journalArticle

  13. 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 journalArticle

  14. 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 journalArticle

  15. 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 journalArticle

  16. 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 journalArticle

  17. 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 journalArticle

  18. 2015
  19. 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 journalArticle

  20. 2014
  21. 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 journalArticle

  22. Published

    Preface

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

    Research output: Contribution to journalArticle

  23. 2013
  24. 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 journalArticle

  25. 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 journalArticle

  26. 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 journalArticle

  27. 2011
  28. 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 journalArticle

  29. 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 journalArticle

  30. 2010
  31. 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 journalArticle

  32. 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 journalArticle

  33. 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 journalArticle

  34. 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 journalArticle

  35. Published
  36. 2008
  37. 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 journalArticle

  38. 2007
  39. 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 journalArticle

  40. 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 journalArticle

  41. 2005
  42. Published

    Denoising Source Separation

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

    Research output: Contribution to journalArticle

  43. 2003
  44. 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 journalArticle

ID: 306157