Markus Heinonen
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  • Aalto SCI Computer Science Konemiehentie 2

20062024

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  • 2024

    Input-gradient space particle inference for neural network ensembles

    Trinh, T., Heinonen, M., Acerbi, L. & Kaski, S., 2024, (Accepted/In press) The Twelfth International Conference on Learning Representations.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    19 Downloads (Pure)
  • 2023

    AbODE: Ab initio antibody design using conjoined ODEs

    Verma, Y., Heinonen, M. & Garg, V., Jul 2023, Proceedings of the 40th International Conference on Machine Learning. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). JMLR, p. 35037-35050 14 p. (Proceedings of Machine Learning Research; vol. 202).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    2 Citations (Scopus)
    35 Downloads (Pure)
  • Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach

    Raj, V., Cui, T., Heinonen, M. & Marttinen, P., 2023, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023. Ruiz, F., Dy, J. & van de Meent, J-W. (eds.). JMLR, p. 6741-6763 (Proceedings of Machine Learning Research; vol. 206).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    28 Downloads (Pure)
  • Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

    Talman, A., Celikkanat, H., Virpioja, S., Heinonen, M. & Tiedemann, J., 2023, Proceedings of the 24th Nordic Conference on Computational Linguistics. University of Tartu Library, p. 358-365

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    32 Downloads (Pure)
  • 2022

    Modular Flows: Differential Molecular Generation

    Verma, Y., Kaski, S., Heinonen, M. & Garg, V., 2022, Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Morgan Kaufmann Publishers, 13 p. (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
  • Tackling covariate shift with node-based Bayesian neural networks

    Trinh, T. Q., Heinonen, M., Acerbi, L. & Kaski, S., 17 Jul 2022, Proceedings of the 39th International Conference on Machine Learning. Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G. & Sabato, S. (eds.). JMLR, p. 21751-21775 25 p. (Proceedings of Machine Learning Research; vol. 162).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    42 Downloads (Pure)
  • Variational multiple shooting for Bayesian ODEs with Gaussian processes

    Hedge, P., Yildiz, C., Lähdesmäki, H., Kaski, S. & Heinonen, M., 2022, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), PMLR. JMLR, p. 790-799 (Proceedings of Machine Learning Research ; vol. 180).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    1 Citation (Scopus)
    53 Downloads (Pure)
  • 2021

    Bayesian Inference for Optimal Transport with Stochastic Cost

    Mallasto, A., Heinonen, M. & Kaski, S., 2021, Proceedings of Asian Conference on Machine Learning. JMLR, p. 1601-1616 16 p. (Proceedings of Machine Learning Research; vol. 157).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    42 Downloads (Pure)
  • Continuous-time Model-based Reinforcement Learning

    Yildiz, C., Heinonen, M. & Lähdesmäki, H., 21 Jul 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. JMLR, p. 12009-12018 (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    67 Downloads (Pure)
  • De-randomizing MCMC dynamics with the diffusion Stein operator

    Shen, Z., Heinonen, M. & Kaski, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Neural Information Processing Systems Foundation, p. 17507-17517 11 p. (Advances in Neural Information Processing Systems; vol. 21).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
    File
    2 Citations (Scopus)
    48 Downloads (Pure)
  • Learning continuous-time PDEs from sparse data with graph neural networks

    Iakovlev, V., Heinonen, M. & Lähdesmäki, H., 2021, International Conference on Learning Representations. OpenReview.net, 15 p.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsProfessional

    Open Access
  • Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations

    Rossi, S., Heinonen, M., Bonilla, E., Shen, Z. & Filippone, M., 2021, 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS). Banerjee, A. & Fukumizu, K. (eds.). Microtome Publishing, 11 p. (Proceedings of Machine Learning Research; vol. 130).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    94 Downloads (Pure)
  • 2020

    Deep Convolutional Gaussian Processes

    Blomqvist, K., Kaski, S. & Heinonen, M., 1 Jan 2020, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M. & Robardet, C. (eds.). Springer, p. 582-597 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11907 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
    26 Citations (Scopus)
  • Learning spectrograms with convolutional spectral kernels

    Shen, Z., Heinonen, M. & Kaski, S., 2020, The 23rd International Conference on Artificial Intelligence and Statistics. Chiappa, S. & Calandra, R. (eds.). JMLR, p. 3826-3836 10 p. (Proceedings of Machine Learning Research; vol. 108).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    47 Downloads (Pure)
  • 2019

    Deep learning with differential Gaussian process flows

    Hegde, P., Heinonen, M., Lähdesmäki, H. & Kaski, S., Apr 2019, The 22nd International Conference on Artificial Intelligence and Statistic. JMLR, Vol. 89. p. 1-15 16 p. (Proceedings of Machine Learning Research; vol. 89).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    15 Citations (Scopus)
    108 Downloads (Pure)
  • Harmonizable mixture kernels with variational Fourier features

    Shen, Z., Heinonen, M. & Kaski, S., May 2019, The 22nd International Conference on Artificial Intelligence and Statistics. JMLR, p. 1812-1821 (Proceedings of Machine Learning Research; vol. 89).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    7 Citations (Scopus)
    93 Downloads (Pure)
  • ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

    Yildiz, C., Heinonen, M. & Lähdesmäki, H., 2019, 33rd Conference on Neural Information Processing Systems: NeurIPS 2019 . Neural Information Processing Systems Foundation, (Advances in Neural Information Processing Systems; vol. 32).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
    91 Citations (Scopus)
  • 2018

    A Nonparametric Spatio-temporal SDE Model

    Yildiz, C., Heinonen, M. & Lähdesmäki, H., 2018, NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Neural Information Processing Systems Foundation, p. 1-5

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsProfessional

  • Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching

    Yildiz, C., Heinonen, M., Intosalmi, J., Mannerström, H. & Lähdesmäki, H., 2018, IEEE International Workshop on Machine Learning for Signal Processing. IEEE, 6 p. 8516991

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    19 Citations (Scopus)
  • Learning unknown ODE models with Gaussian processes

    Heinonen, M., Yildiz, C., Mannerström, H., Intosalmi, J. & Lähdesmäki, H., 2018, Proceedings of the 35th International Conference on Machine Learning, ICML 2018. International Machine Learning Society , Vol. 5. p. 3120-3132 13 p. (Proceedings of Machine Learning Research; vol. 80).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    10 Citations (Scopus)
    135 Downloads (Pure)
  • Variational zero-inflated Gaussian processes with sparse kernels

    Hegde, P., Heinonen, M. & Kaski, S., 2018, 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. Association for Uncertainty in Artificial Intelligence, Vol. 1. p. 361-371 148

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
    4 Citations (Scopus)
  • 2017

    A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

    Remes, S., Heinonen, M. & Kaski, S., Nov 2017, Proceedings of the 9th Asian Conference on Machine Learning. Zhang, M-L. & Noh, Y-K. (eds.). JMLR, p. 455-470 16 p. (Proceedings of Machine Learning Research; vol. 77).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    2 Citations (Scopus)
    73 Downloads (Pure)
  • Non-Stationary Spectral Kernels

    Remes, S., Heinonen, M. & Kaski, S., 2017, Advances in Neural Information Processing Systems 30: Proceedings of NIPS2017. Curran Associates Inc., p. 4645-4654 (Advances in Neural Information Processing Systems; vol. 30).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
    55 Citations (Scopus)
  • 2016

    Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

    Heinonen, M., Mannerström, H., Rousu, J., Kaski, S. & Lähdesmäki, H., 2016, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics: JMLR: W&CP. JMLR, p. 732-740 9 p. ( JMLR: Workshop and Conference Proceedings; vol. 51).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

  • Random Fourier Features For Operator-Valued Kernels

    Brault, R., Heinonen, M. & d'Alché-Buc, F., 2016, Proceedings of the 8th Asian Conference on Machine Learning. Durrant, B. & Kim, K-E. (eds.). JMLR, p. 110-125 (Proceedings of Machine Learning Research; vol. 63).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
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    121 Downloads (Pure)
  • 2012

    Efficient Path Kernels for Reaction Function Prediction

    Heinonen, M., Välimäki, N., Mäkinen, V. & Rousu, J., 2012, Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. p. 202-207

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

  • 2010

    Multilabel classification of drug-like molecules via max-margin conditional random fields

    Su, H., Heinonen, M. & Rousu, J., 2010, Proceedings of The Fifth European Workshop on Probabilistic Graphical Models (PGM-2010): 13-15 September, 2010, Helsinki, Finland. Myllymäki, P., Roos, T. & Jaakkola, T. (eds.). Helsinki Institute for Information Technology HIIT, p. 265-272 (HIIT Publications; vol. 2010, no. 2).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

  • Structured output prediction of anti-cancer drug activity

    Su, H., Heinonen, M. & Rousu, J., 2010, Pattern Recognition in Bioinformatics: 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings. Dijkstra, T. M. H., Tsivtsivadze, E., Marchiori, E. & Heskes, T. (eds.). Springer, p. 38-49 ( Lecture Notes in Computer Science; vol. 6282).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

  • 2006

    Ab Initio prediction of molecular fragments from tandem mass spectrometry data

    Heinonen, M., Rantanen, A., Mielikäinen, T., Pitkänen, E., Kokkonen, J. T. & Rousu, J., 2006, Proceedings of the German Conference on Bioinformatics. Huson, D., Kohlbacher, O., Lupas, A., Nieselt, K. & Zell, A. (eds.). Gesellschaft für Informatik , p. 40-53 (Lecture Notes in Informatics (LNI); vol. P83).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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