Publications

Manuscripts

• Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families V. Simkus, and M. Gutmann arXiv:2403.03069 2024

  @article{Simkus2024a,
    author = {Simkus, Vaidotas and Gutmann, Michael U.},
    journal = {arXiv:2403.03069},
    title = {Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families},
    year = {2024},
    arxiv = {https://arxiv.org/abs/2403.03069},
    keywords = {variational autoencoders, missing data, incomplete data, mixture models}
  }
  

Refereed papers

• Designing optimal behavioral experiments using machine learning S. Valentin, S. Kleinegesse, N. Bramley, P. Seriès, M. Gutmann, and C. Lucas eLife 2024

  @article{Valentin2024,
    author = {Valentin, Simon and Kleinegesse, Steven and Bramley, Neil R. and Seri\`es, Peggy and Gutmann, Michael U. and Lucas, Christopher G.},
    journal = {eLife},
    title = {Designing optimal behavioral experiments using machine learning},
    year = {2024},
    month = jan,
    pages = {e86224},
    volume = {13},
    arxiv = {https://arxiv.org/abs/2305.07721},
    doi = {https://doi.org/10.7554/eLife.86224},
    editor = {Gold, Joshua I.},
    issn = {2050-084X},
    keywords = {experimental design, machine learning, computational modeling},
    publisher = {eLife Sciences Publications, Ltd},
    url = {https://doi.org/10.7554/eLife.86224},
    month_numeric = {1}
  }
  

• An Extendable Python Implementation of Robust Optimisation Monte Carlo V. Gkolemis, M. Gutmann, and H. Pesonen Journal of Statistical Software 2023

  @article{Gkolemis2023a,
    author = {Gkolemis, Vasilis and Gutmann, Michael and Pesonen, Henri},
    journal = {Journal of Statistical Software},
    title = {An Extendable {P}ython Implementation of {R}obust {O}ptimisation {M}onte {C}arlo},
    year = {2023},
    month = sep,
    pages = {1--25},
    arxiv = {https://arxiv.org/abs/2309.10612},
    day = {15},
    issn = {1548-7660},
    keywords = {Bayesian inference, implicit models, likelihood-free, Python, ELFI},
    language = {English},
    publisher = {University of California at Los Angeles},
    month_numeric = {9}
  }
  

• Bayesian Optimization with Informative Covariance A. Eduardo, and M. Gutmann Transactions on Machine Learning Research 2023

  @article{Eduardo2023a,
    author = {Eduardo, Afonso and Gutmann, Michael U.},
    journal = {Transactions on Machine Learning Research},
    title = {Bayesian Optimization with Informative Covariance},
    year = {2023},
    arxiv = {https://arxiv.org/abs/2208.02704},
    issn = {2835-8856},
    keywords = {Bayesian Optimization, Gaussian processes},
    url = {https://openreview.net/forum?id=JwgVBv18RG}
  }
  

• Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression A. Srivastava, S. Han, K. Xu, B. Rhodes, and M. Gutmann Transactions on Machine Learning Research 2023

  @article{Srivastava2023a,
    author = {Srivastava, Akash and Han, Seungwook and Xu, Kai and Rhodes, Benjamin and Gutmann, Michael U.},
    journal = {Transactions on Machine Learning Research},
    title = {Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression},
    year = {2023},
    arxiv = {https://arxiv.org/abs/2305.00869},
    issn = {2835-8856},
    url = {https://openreview.net/forum?id=jM8nzUzBWr}
  }
  

• Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data V. Simkus, B. Rhodes, and M. Gutmann Journal of Machine Learning Research 2023

  @article{Simkus2023a,
    author = {Simkus, Vaidotas and Rhodes, Benjamin and Gutmann, Michael U.},
    journal = {Journal of Machine Learning Research},
    title = {Variational {{Gibbs}} Inference for Statistical Model Estimation from Incomplete Data},
    year = {2023},
    number = {196},
    pages = {1--72},
    volume = {24},
    arxiv = {https://arxiv.org/abs/2111.13180},
    keywords = {Statistical Model Estimation, Variational Inference, Gibbs Sampling, Missing Data, Amortised Inference},
    url = {http://jmlr.org/papers/v24/21-1373.html}
  }
  

• Is Learning Summary Statistics Necessary for Likelihood-free Inference? Y. Chen, M. Gutmann, and A. Weller Proceedings of the 40th International Conference on Machine Learning (ICML) 2023

  @article{Chen2023a,
    author = {Chen, Yanzhi and Gutmann, Michael U. and Weller, Adrian},
    journal = {Proceedings of the 40th International Conference on Machine Learning (ICML)},
    title = {Is Learning Summary Statistics Necessary for Likelihood-free Inference?},
    year = {2023},
    url = {https://openreview.net/forum?id=jjzJ768iV1}
  }
  

• Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling V. Simkus, and M. Gutmann Transactions on Machine Learning Research 2023

  @article{Simkus2023b,
    author = {Simkus, Vaidotas and Gutmann, Michael U.},
    journal = {Transactions on Machine Learning Research},
    title = {Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling},
    year = {2023},
    arxiv = {https://arxiv.org/abs/2308.09078},
    issn = {2835-8856},
    keywords = {Variational Autoencoder, MCMC, Importance Sampling, Probabilistic Inference, Missing Data},
    url = {https://openreview.net/forum?id=I5sJ6PU6JN}
  }
  

• Systematic comparison of ranking aggregation methods for gene lists in experimental results B. Wang, A. Law, T. Regan, N. Parkinson, J. Cole, C. Russell, D. Dockrell, M. Gutmann, and J. Baillie Bioinformatics 2022

  @article{Wang2022a,
    author = {Wang, Bo and Law, Andy and Regan, Tim and Parkinson, Nicholas and Cole, Joby and Russell, Clark D. and Dockrell, David H. and Gutmann, Michael U. and Baillie, J. Kenneth},
    journal = {Bioinformatics},
    title = {Systematic comparison of ranking aggregation methods for gene lists in experimental results},
    year = {2022},
    month = sep,
    number = {21},
    pages = {4927--4933},
    volume = {38},
    arxiv = {https://www.biorxiv.org/content/early/2022/01/10/2022.01.09.475491},
    issn = {1367-4803},
    url = {https://doi.org/10.1093/bioinformatics/btac621},
    month_numeric = {9}
  }
  

• Statistical applications of contrastive learning M. Gutmann, S. Kleinegesse, and B. Rhodes Behaviormetrika 2022

  @article{Gutmann2022a,
    author = {Gutmann, Michael U. and Kleinegesse, Steven and Rhodes, Benjamin},
    journal = {Behaviormetrika},
    title = {Statistical applications of contrastive learning},
    year = {2022},
    month = jun,
    arxiv = {https://arxiv.org/abs/2204.13999},
    issn = {1349-6964},
    refid = {Gutmann2022},
    url = {https://doi.org/10.1007/s41237-022-00168-w},
    month_numeric = {6}
  }
  

• Inference and uncertainty quantification of stochastic gene expression via synthetic models K. Öcal, M. Gutmann, G. Sanguinetti, and R. Grima Journal of The Royal Society Interface 2022

  @article{Ocal2022,
    author = {\"Ocal, Kaan and Gutmann, Michael U. and Sanguinetti, Guido and Grima, Ramon},
    journal = {Journal of The Royal Society Interface},
    title = {Inference and uncertainty quantification of stochastic gene expression via synthetic models},
    year = {2022},
    number = {192},
    pages = {20220153},
    volume = {19},
    arxiv = {https://www.biorxiv.org/content/10.1101/2022.01.25.477666v1},
    doi = {10.1098/rsif.2022.0153},
    publisher = {Royal Society},
    url = {https://doi.org/10.1098/rsif.2022.0153}
  }
  

• Enhanced gradient-based MCMC in discrete spaces B. Rhodes, and M. Gutmann Transactions on Machine Learning Research 2022

  @article{Rhodes2022a,
    author = {Rhodes, Benjamin and Gutmann, Michael U.},
    journal = {Transactions on Machine Learning Research},
    title = {Enhanced gradient-based {MCMC} in discrete spaces},
    year = {2022},
    arxiv = {https://arxiv.org/abs/2208.00040},
    url = {https://openreview.net/forum?id=j2Mid5hFUJ}
  }
  

• Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations M. Järvenpää, M. Gutmann, A. Vehtari, and P. Marttinen Bayesian Analysis 2021

  @article{Jarvenpaa2020,
    author = {J\"arvenp\"a\"a, M. and Gutmann, M. U. and Vehtari, A. and Marttinen, P.},
    journal = {Bayesian Analysis},
    title = {Parallel {G}aussian process surrogate {B}ayesian inference with noisy likelihood evaluations},
    year = {2021},
    number = {1},
    pages = {147--178},
    volume = {16},
    arxiv = {https://arxiv.org/abs/1905.01252},
    doi = {10.1214/20-BA1200},
    keywords = {expensive likelihoods, Gaussian processes, likelihood-free inference, parallel computing, sequential experiment design, surrogate modelling},
    publisher = {International Society for Bayesian Analysis},
    url = {https://doi.org/10.1214/20-BA1200}
  }
  

• Adaptive Approximate Bayesian Computation Tolerance Selection U. Simola, J. Cisewski-Kehe, M. Gutmann, and J. Corander Bayesian Analysis 2021

  @article{Simola2020,
    author = {Simola, U. and Cisewski-Kehe, J. and Gutmann, M. U. and Corander, J.},
    journal = {Bayesian Analysis},
    title = {Adaptive Approximate {B}ayesian Computation Tolerance Selection},
    year = {2021},
    number = {2},
    pages = {397--423},
    volume = {16},
    arxiv = {https://arxiv.org/abs/1907.01505},
    doi = {10.1214/20-BA1211},
    keywords = {complex stochastic modeling, likelihood-free methods, sequential Monte Carlo},
    publisher = {International Society for Bayesian Analysis},
    url = {https://doi.org/10.1214/20-BA1211}
  }
  

• Sequential Bayesian Experimental Design for Implicit Models via Mutual Information S. Kleinegesse, C. Drovandi, and M. Gutmann Bayesian Analysis 2021

  @article{Kleinegesse2020b,
    author = {Kleinegesse, S. and Drovandi, C. and Gutmann, M. U.},
    journal = {Bayesian Analysis},
    title = {Sequential {B}ayesian Experimental Design for Implicit Models via Mutual Information},
    year = {2021},
    number = {16},
    pages = {773--802},
    volume = {3},
    arxiv = {https://arxiv.org/abs/2003.09379},
    doi = {10.1214/20-BA1225},
    keywords = {Approximate Bayesian Computation, Bayesian experimental design, implicit models, likelihood-free inference, mutual information},
    publisher = {International Society for Bayesian Analysis}
  }
  

• Neural Approximate Sufficient Statistics for Implicit Models Y. Chen, D. Zhang, M. Gutmann, A. Courville, and Z. Zhu In International Conference on Learning Representations (ICLR) 2021

  @inproceedings{Chen2021a,
    author = {Chen, Y. and Zhang, D. and Gutmann, M. U. and Courville, A. and Zhu, Z.},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {Neural Approximate Sufficient Statistics for Implicit Models},
    year = {2021},
    arxiv = {https://arxiv.org/abs/2010.10079},
    keywords = {Sufficient statistics, mutual information, likelihood-free inference, implicit models, approximate Bayesian inference},
    url = {https://openreview.net/forum?id=SRDuJssQud}
  }
  

• Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods D. Ivanova, A. Foster, S. Kleinegesse, M. Gutmann, and T. Rainforth In Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeuRIPS 2021) 2021

  @inproceedings{Ivanova2021,
    author = {Ivanova, Desi R and Foster, Adam and Kleinegesse, Steven and Gutmann, Michael U and Rainforth, Tom},
    booktitle = {Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeuRIPS 2021)},
    title = {Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods},
    year = {2021},
    publisher = {Neural Information Processing Systems},
    arxiv = {https://arxiv.org/abs/2111.02329},
    keywords = {Experimental design},
    url = {https://papers.nips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html}
  }
  

• Bayesian Optimal Experimental Design for Simulator Models of Cognition S. Valentin, S. Kleinegesse, N. Bramley, M. Gutmann, and C. Lucas In NeurIPS 2021 Workshop "AI for Science" 2021

  @conference{Valentin2021a,
    author = {Valentin, Simon and Kleinegesse, Steven and Bramley, Neil R and Gutmann, Michael U and Lucas, Christopher G},
    title = {Bayesian Optimal Experimental Design for Simulator Models of Cognition},
    booktitle = {NeurIPS 2021 Workshop {"}AI for Science{"}},
    year = {2021},
    arxiv = {https://arxiv.org/abs/2110.15632}
  }
  

• Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions T. Lopez Guevara, R. Pucci, N. Taylor, M. Gutmann, R. Ramamoorthy, and K. Subr In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) 2020

  @inproceedings{LopezGuevara2020,
    author = {{Lopez Guevara}, Tatiana and Pucci, Rita and Taylor, Nicholas and Gutmann, {Michael U.} and Ramamoorthy, Ram and Subr, Kartic},
    booktitle = {Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)},
    title = {Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions},
    year = {2020},
    month = jul,
    day = {1},
    language = {English},
    url = {https://www.iros2020.org/index.html},
    month_numeric = {7}
  }
  

• Genome-wide CRISPR screen Identifies Host Dependency Factors for Influenza A Virus Infection B. Li, S. Clohisey, B. Chia, B. Wang, A. Cui, T. Eisenhaure, L. Schweitzer, P. Hoover, N. Parkinson, A. Nachshon, N. Smith, T. Regan, D. Farr, M. Gutmann, S. Bukhari, A. Law, M. Sangesland, I. Gat-viks, P. Digard, S. Vasudevan, D. Lingwood, D. Dockrell, J. Doench, J. Baillie, and N. Hacohen Nature Communications 2020

  @article{Li2020,
    title = {Genome-wide CRISPR screen Identifies Host Dependency Factors for Influenza A Virus Infection},
    author = {Li, Bo and Clohisey, Sara M. and Chia, Bing Shao and Wang, Bo and Cui, Ang and Eisenhaure, Thomas and Schweitzer, Lawrence D. and Hoover, Paul and Parkinson, Nicholas J. and Nachshon, Aharon and Smith, Nikki and Regan, Tim and Farr, David and Gutmann, Michael U. and Bukhari, Syed Irfan and Law, Andrew and Sangesland, Maya and Gat-viks, Irit and Digard, Paul and Vasudevan, Shobha and Lingwood, Daniel and Dockrell, David H. and Doench, John G. and Baillie, J. Kenneth and Hacohen, Nir},
    journal = {Nature Communications},
    year = {2020},
    month = jan,
    number = {1},
    pages = {164--},
    volume = {11},
    doi = {https://doi.org/10.1038/s41467-019-13965-x},
    issn = {2041-1723},
    url = {https://doi.org/10.1038/s41467-019-13965-x},
    month_numeric = {1}
  }
  

• Generative Ratio Matching Networks A. Srivastava, K. Xu, M. Gutmann, and C. Sutton In Proceedings of the International Conference on Learning Representations (ICLR) 2020

  @inproceedings{Srivastava2020,
    author = {Srivastava, A. and Xu, K. and Gutmann, M. U. and Sutton, C.},
    booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
    title = {Generative Ratio Matching Networks},
    year = {2020},
    arxiv = {https://arxiv.org/abs/1806.00101},
    url = {https://openreview.net/forum?id=SJg7spEYDS}
  }
  

• Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation S. Kleinegesse, and M. Gutmann In Proceedings of the 37th International Conference on Machine Learning (ICML) 2020

  @inproceedings{Kleinegesse2020a,
    author = {Kleinegesse, S. and Gutmann, M. U.},
    booktitle = {Proceedings of the 37th International Conference on Machine Learning (ICML)},
    title = {Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation},
    year = {2020},
    editor = {Daum\'e, Hal III and Singh, Aarti},
    month = {13--18 Jul},
    pages = {5316--5326},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    volume = {119},
    arxiv = {https://arxiv.org/abs/2002.08129},
    pdf = {http://proceedings.mlr.press/v119/kleinegesse20a/kleinegesse20a.pdf},
    url = {http://proceedings.mlr.press/v119/kleinegesse20a.html}
  }
  

• Molecular Patterns in Acute Pancreatitis Reflect Generalizable Endotypes of the Host Response to Systemic Injury in Humans L. Neyton, X. Zheng, C. Skouras, A. Doeschl-Wilson, M. Gutmann, I. Uings, F. Rao, A. Nicolas, C. Marshall, L. Wilson, J. Baillie, and D. Mole Annals of Surgery 2020

  @article{Neyton2020,
    author = {Neyton, Lucile P. A. and Zheng, Xiaozhong and Skouras, Christos and Doeschl-Wilson, Andrea and Gutmann, Michael U. and Uings, Iain and Rao, Francesco V. and Nicolas, Armel and Marshall, Craig and Wilson, Lisa-Marie and Baillie, J. Kenneth and Mole, Damian J.},
    journal = {Annals of Surgery},
    title = {Molecular Patterns in Acute Pancreatitis Reflect Generalizable Endotypes of the Host Response to Systemic Injury in Humans},
    year = {2020},
    number = {in press},
    arxiv = {https://www.biorxiv.org/content/10.1101/539569v3},
    doi = {https://doi.org/10.1101/539569},
    issn = {0003-4932},
    keywords = {acute pancreatitis, cluster analysis, critical illness trajectory, endotypes, multiomics, time series},
    refid = {00000658-900000000-94475},
    url = {https://journals.lww.com/annalsofsurgery/Fulltext/9000/Molecular_Patterns_in_Acute_Pancreatitis_Reflect.94475.aspx}
  }
  

• Robust Optimisation Monte Carlo B. Ikonomov, and M. Gutmann In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS) 2020

  @inproceedings{Ikonomov2020,
    author = {Ikonomov, B. and Gutmann, M. U.},
    booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {Robust Optimisation {M}onte {C}arlo},
    year = {2020},
    address = {Online},
    editor = {Chiappa, Silvia and Calandra, Roberto},
    month = {26--28 Aug},
    pages = {2819--2829},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    volume = {108},
    arxiv = {https://arxiv.org/abs/1904.00670},
    pdf = {http://proceedings.mlr.press/v108/ikonomov20a/ikonomov20a.pdf},
    url = {http://proceedings.mlr.press/v108/ikonomov20a.html}
  }
  

• Likelihood-Free Inference by Ratio Estimation O. Thomas, R. Dutta, J. Corander, S. Kaski, and M. Gutmann Bayesian Analysis 2020

  @article{Thomas2020,
    author = {Thomas, O. and Dutta, R. and Corander, J. and Kaski, S. and Gutmann, M. U.},
    journal = {Bayesian Analysis},
    title = {Likelihood-Free Inference by Ratio Estimation},
    year = {2020},
    number = {advance publication},
    arxiv = {https://arxiv.org/abs/1611.10242},
    doi = {https://doi.org/10.1214/20-BA1238},
    keywords = {approximate Bayesian computation, synthetic likelihood, summary statistics selection, density-ratio estimation; logistic regression; probabilistic classification, stochastic dynamical systems},
    url = {https://projecteuclid.org/journals/bayesian-analysis/volume-17/issue-1/Likelihood-Free-Inference-by-Ratio-Estimation/10.1214/20-BA1238.full}
  }
  

• Telescoping Density-Ratio Estimation B. Rhodes, K. Xu, and M. Gutmann In Advances in Neural Information Processing Systems 34 (NeurIPS 2020) 2020

  @inproceedings{Rhodes2020a,
    author = {Rhodes, B. and Xu, K. and Gutmann, M. U.},
    booktitle = {Advances in Neural Information Processing Systems 34 (NeurIPS 2020)},
    title = {Telescoping Density-Ratio Estimation},
    year = {2020},
    editor = {Larochelle, H. and Ranzato, M. and Hadsell, R. and Balcan, M. F. and Lin, H.},
    note = {(Spotlight, top 4\% of submissions)},
    pages = {4905--4916},
    publisher = {Curran Associates, Inc.},
    volume = {33},
    arxiv = {https://arxiv.org/abs/2006.12204},
    keywords = {density ratio estimation, mutual information, energy-based models},
    url = {https://papers.nips.cc/paper/2020/hash/33d3b157ddc0896addfb22fa2a519097-Abstract.html}
  }
  

• Adaptive Gaussian Copula ABC Y. Chen, and M. Gutmann In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2019

  @inproceedings{Chen2019,
    author = {Chen, Y. and Gutmann, M. U.},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {Adaptive {G}aussian Copula {ABC}},
    year = {2019},
    editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
    month = {16--18 Apr},
    number = {89},
    pages = {1584--1592},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    volume = {89},
    arxiv = {https://arxiv.org/abs/1902.10704},
    url = {http://proceedings.mlr.press/v89/chen19d}
  }
  

• Efficient acquisition rules for model-based approximate Bayesian computation M. Järvenpää, M. Gutmann, A. Vehtari, and P. Marttinen Bayesian Analysis 2019

  @article{Jarvenpaa2019a,
    author = {J\"arvenp\"a\"a, M. and Gutmann, M. U. and Vehtari, A. and Marttinen, P.},
    journal = {Bayesian Analysis},
    title = {Efficient acquisition rules for model-based approximate {B}ayesian computation},
    year = {2019},
    number = {2},
    pages = {595--622},
    volume = {14},
    arxiv = {https://arxiv.org/abs/1704.00520},
    doi = {doi:10.1214/18-BA1121},
    keywords = {Approximation Bayesian computation, Bayesian optimisation},
    url = {https://projecteuclid.org/euclid.ba/1537258134}
  }
  

• Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth-death models J. Lintusaari, P. Blomstedt, B. Rose, T. Sivula, M. Gutmann, S. Kaski, and J. Corander Wellcome Open Research 2019

  @article{Lintusaari2019a,
    author = {Lintusaari, J and Blomstedt, P and Rose, B and Sivula, T and Gutmann, M. U. and Kaski, S and Corander, J},
    journal = {Wellcome Open Research},
    title = {Resolving outbreak dynamics using approximate {B}ayesian computation for stochastic birth-death models},
    year = {2019},
    number = {14},
    volume = {4},
    arxiv = {https://www.biorxiv.org/content/10.1101/215533v2},
    doi = {10.12688/wellcomeopenres.15048.2},
    url = {https://wellcomeopenresearch.org/articles/4-14/v2}
  }
  

• Variational Noise-Contrastive Estimation B. Rhodes, and M. Gutmann In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2019

  @inproceedings{Rhodes2019,
    author = {Rhodes, B. and Gutmann, M. U.},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {Variational Noise-Contrastive Estimation},
    year = {2019},
    editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
    month = {16--18 Apr},
    number = {89},
    pages = {1584--1592},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    volume = {89},
    arxiv = {https://arxiv.org/abs/1810.08010},
    keywords = {noise-contrastive estimation, latent variables},
    url = {http://proceedings.mlr.press/v89/rhodes19a}
  }
  

• Bayesian inference of atomistic structure in functional materials M. Todorović, M. Gutmann, J. Corander, and P. Rinke npj Computational Materials 2019

  @article{Todorovic2019,
    author = {Todorovi\'c, M. and Gutmann, M. U. and Corander, J. and Rinke, P.},
    journal = {npj Computational Materials},
    title = {Bayesian inference of atomistic structure in functional materials},
    year = {2019},
    number = {1},
    pages = {35},
    volume = {5},
    arxiv = {https://arxiv.org/abs/1708.09274},
    doi = {https://doi.org/10.1038/s41524-019-0175-2},
    issn = {2057-3960},
    refid = {Todorovic2019},
    url = {https://www.nature.com/articles/s41524-019-0175-2}
  }
  

• Efficient Bayesian Experimental Design for Implicit Models S. Kleinegesse, and M. Gutmann In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2019

  @inproceedings{Kleinegesse2019,
    author = {Kleinegesse, S. and Gutmann, M. U.},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {Efficient {B}ayesian Experimental Design for Implicit Models},
    year = {2019},
    editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
    month = {16--18 Apr},
    number = {89},
    pages = {1584--1592},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    volume = {89},
    arxiv = {https://arxiv.org/abs/1810.09912},
    url = {http://proceedings.mlr.press/v89/kleinegesse19a}
  }
  

• Weak Epistasis May Drive Adaptation in Recombining Bacteria B. Arnold, M. Gutmann, Y. Grad, S. Sheppard, J. Corander, M. Lipsitch, and W. Hanage Genetics 2018

  @article{Arnold2018,
    author = {Arnold, B.J. and Gutmann, M. U. and Grad, Y.H. and Sheppard, S.K. and Corander, J. and Lipsitch, M. and Hanage, W.P.},
    journal = {Genetics},
    title = {Weak Epistasis May Drive Adaptation in Recombining Bacteria},
    year = {2018},
    month = jan,
    number = {3},
    pages = {1247--1260},
    volume = {208},
    arxiv = {https://www.biorxiv.org/content/10.1101/119958v1},
    doi = {https://doi.org/10.1534/genetics.117.300662},
    url = {https://www.genetics.org/content/208/3/1247},
    month_numeric = {1}
  }
  

• Conditional Noise-Contrastive Estimation of Unnormalised Models C. Ceylan, and M. Gutmann In Proceedings of the 35th International Conference on Machine Learning (ICML) 2018

  @inproceedings{Ceylan2018,
    author = {Ceylan, C. and Gutmann, M. U.},
    booktitle = {Proceedings of the 35th International Conference on Machine Learning (ICML)},
    title = {Conditional Noise-Contrastive Estimation of Unnormalised Models},
    year = {2018},
    editor = {Dy, Jennifer and Krause, Andreas},
    pages = {725--733},
    series = {Proceedings of Machine Learning Research},
    volume = {80},
    arxiv = {https://arxiv.org/abs/1806.03664},
    keywords = {unnormalised models, noise-contrastive estimation},
    pdf = {http://proceedings.mlr.press/v80/ceylan18a/ceylan18a.pdf},
    url = {http://proceedings.mlr.press/v80/ceylan18a.html}
  }
  

• Likelihood-free inference via classification M. Gutmann, R. Dutta, S. Kaski, and J. Corander Statistics and Computing 2018

  @article{Gutmann2018,
    author = {Gutmann, M. U. and Dutta, R. and Kaski, S. and Corander, J.},
    journal = {Statistics and Computing},
    title = {Likelihood-free inference via classification},
    year = {2018},
    number = {2},
    pages = {411--425},
    volume = {28},
    arxiv = {https://arxiv.org/abs/1407.4981},
    day = {13},
    doi = {10.1007/s11222-017-9738-6},
    issn = {1573-1375},
    keywords = {Intractable likelihoods, generative models, latent variables, approximate Bayesian computation},
    url = {https://doi.org/10.1007/s11222-017-9738-6}
  }
  

• Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria M. Järvenpää, M. Gutmann, A. Vehtari, and P. Marttinen The Annals of Applied Statistics 2018

  @article{Jarvenpaa2018,
    author = {J\"arvenp\"a\"a, M. and Gutmann, M. U. and Vehtari, A. and Marttinen, P.},
    journal = {The Annals of Applied Statistics},
    title = {Gaussian process modeling in approximate {B}ayesian computation to estimate horizontal gene transfer in bacteria},
    year = {2018},
    number = {4},
    pages = {2228--2251},
    volume = {12},
    arxiv = {https://arxiv.org/abs/1610.06462},
    doi = {https://doi.org/10.1214/18-AOAS1150},
    keywords = {Approximation Bayesian computation, emulation, Gaussian processes, model selection},
    url = {https://projecteuclid.org/euclid.aoas/1542078043}
  }
  

• ELFI: Engine for Likelihood-Free Inference J. Lintusaari, H. Vuollekoski, A. Kangasrääsiö, K. Skytén, M. Järvenpää, P. Marttinen, M. Gutmann, A. Vehtari, and J. Corander Journal of Machine Learning Research 2018

  @article{Lintusaari2018,
    author = {Lintusaari, Jarno and Vuollekoski, Henri and Kangasr\"a\"asi\"o, Antti and Skyt\'en, Kusti and J\"arvenp\"a\"a, Marko and Marttinen, Pekka and Gutmann, Michael U. and Vehtari, Aki and Corander, Jukka},
    journal = {Journal of Machine Learning Research},
    title = {{ELFI}: Engine for Likelihood-Free Inference},
    year = {2018},
    number = {16},
    pages = {1--7},
    volume = {19},
    arxiv = {https://arxiv.org/abs/1708.00707},
    keywords = {Likelihood-free inference, approximate Bayesian computation, Python, software, parallel computing},
    url = {http://jmlr.org/papers/v19/17-374.html}
  }
  

• Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation T. Lopez-Guevara, N. Taylor, M. Gutmann, S. Ramamoorthy, and K. Subr In Proceedings of the 1st Annual Conference on Robot Learning (CoRL) 2017

  @inproceedings{LopezGuevara2017a,
    author = {Lopez-Guevara, T. and Taylor, N.K. and Gutmann, M. U. and Ramamoorthy, S. and Subr, K.},
    booktitle = {Proceedings of the 1st Annual Conference on Robot Learning (CoRL)},
    title = {Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation},
    year = {2017},
    editor = {Levine, Sergey and Vanhoucke, Vincent and Goldberg, Ken},
    month = nov,
    pages = {77--86},
    series = {Proceedings of Machine Learning Research},
    volume = {78},
    pdf = {http://proceedings.mlr.press/v78/lopez-guevara17a/lopez-guevara17a.pdf},
    url = {http://proceedings.mlr.press/v78/lopez-guevara17a.html},
    month_numeric = {11}
  }
  

• Fundamentals and Recent Developments in Approximate Bayesian Computation J. Lintusaari, M. Gutmann, R. Dutta, S. Kaski, and J. Corander Systematic Biology 2017

  @article{Lintusaari2017,
    author = {Lintusaari, J. and Gutmann, M. U. and Dutta, R. and Kaski, S. and Corander, J.},
    journal = {Systematic Biology},
    title = {Fundamentals and Recent Developments in Approximate {B}ayesian Computation},
    year = {2017},
    month = jan,
    number = {1},
    pages = {e66--e82},
    volume = {66},
    doi = {10.1093/sysbio/syw077},
    issn = {1063-5157},
    keywords = {Intractable likelihoods, generative models, latent variables, approximate Bayesian computation},
    url = {http://dx.doi.org/10.1093/sysbio/syw077},
    month_numeric = {1}
  }
  

• Frequency-dependent selection in vaccine-associated pneumococcal population dynamics J. Corander, C. Fraser, M. Gutmann, B. Arnold, W. Hanage, S. Bentley, M. Lipsitch, and N. Croucher Nature Ecology & Evolution 2017

  @article{Corander2017,
    author = {Corander, J. and Fraser, C. and Gutmann, M. U. and Arnold, B. and Hanage, W.P. and Bentley, S.D. and Lipsitch, M. and Croucher, N.J.},
    journal = {Nature Ecology \& Evolution},
    title = {Frequency-dependent selection in vaccine-associated pneumococcal population dynamics},
    year = {2017},
    pages = {1950--1960},
    volume = {1},
    doi = {https://doi.org/10.1038/s41559-017-0337-x},
    issn = {2397-334X},
    refid = {Corander2017},
    url = {https://www.nature.com/articles/s41559-017-0337-x}
  }
  

• Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen Neural Computation 2017

  @article{Sasaki2017,
    author = {Sasaki, H. and Gutmann, M. U. and Shouno, H. and Hyv\"arinen, A.},
    journal = {Neural Computation},
    title = {Simultaneous Estimation of Non-{G}aussian Components and their Correlation Structure},
    year = {2017},
    number = {11},
    pages = {2887--2924},
    volume = {29},
    arxiv = {https://arxiv.org/abs/1506.05666},
    doi = {10.1162/neco_a_01006},
    keywords = {Extensions of ICA, speech data, natural images},
    url = {http://dx.doi.org/10.1162/neco_a_01006}
  }
  

• VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning A. Srivastava, A. Valkov, C. Russell, M. Gutmann, and C. Sutton In Advances in Neural Information Processing Systems 30 (NIPS 2017) 2017

  @inproceedings{Srivastava2017,
    author = {Srivastava, A. and Valkov, A. and Russell, C. and Gutmann, M. U. and Sutton, C.},
    booktitle = {Advances in Neural Information Processing Systems 30 (NIPS 2017)},
    title = {{VEEGAN}: Reducing Mode Collapse in {GAN}s using Implicit Variational Learning},
    year = {2017},
    arxiv = {https://arxiv.org/abs/1705.07761},
    journal = {Advances in Neural Information Processing Systems 30 (NIPS 2017)},
    url = {http://papers.nips.cc/paper/6923-veegan-reducing-mode-collapse-in-gans-using-implicit-variational-learning}
  }
  

• Bayesian inference of physiologically meaningful parameters from body sway measurements A. Tietäväinen, M. Gutmann, E. Keski-Vakkuri, J. Corander, and E. Haeggström Scientific Reports 2017

  @article{Tietavainen2017,
    author = {Tiet\"av\"ainen, A. and Gutmann, M. U. and Keski-Vakkuri, E. and Corander, J. and Haeggstr\"om, E.},
    journal = {Scientific Reports},
    title = {Bayesian inference of physiologically meaningful parameters from body sway measurements},
    year = {2017},
    number = {3771},
    pages = {1--14},
    volume = {7},
    doi = {https://doi.org/10.1038/s41598-017-02372-1},
    issn = {2045-2322},
    keywords = {Approximate Bayesian inference, nonlinear dynamical systems, balance and muscular control},
    url = {https://www.nature.com/articles/s41598-017-02372-1}
  }
  

• Bayesian optimization for likelihood-free inference of simulator-based statistical models M. Gutmann, and J. Corander Journal of Machine Learning Research 2016

  @article{Gutmann2016a,
    author = {Gutmann, M. U. and Corander, J},
    journal = {Journal of Machine Learning Research},
    title = {Bayesian optimization for likelihood-free inference of simulator-based statistical models},
    year = {2016},
    number = {125},
    pages = {1--47},
    volume = {17},
    arxiv = {http://arxiv.org/abs/1501.03291},
    keywords = {Intractable likelihoods, generative models, latent variables, approximate Bayesian computation},
    url = {http://jmlr.org/papers/v17/15-017.html}
  }
  

• On the identifiability of transmission dynamic models for infectious diseases J. Lintusaari, M. Gutmann, S. Kaski, and J. Corander Genetics 2016

  @article{Lintusaari2016,
    author = {Lintusaari, J. and Gutmann, M. U. and Kaski, S. and Corander, J.},
    journal = {Genetics},
    title = {On the identifiability of transmission dynamic models for infectious diseases},
    year = {2016},
    number = {3},
    pages = {911--918},
    volume = {202},
    arxiv = {https://www.biorxiv.org/content/10.1101/021972v2},
    doi = {https://doi.org/10.1534/genetics.115.180034},
    keywords = {Intractable likelihoods, epidemiology of infectious disease},
    url = {https://www.genetics.org/content/202/3/911}
  }
  

• The impact of host metapopulation structure on the population genetics of colonizing bacteria E. Numminen, M. Gutmann, M. Shubin, P. Marttinen, G. Méric, W. Schaik, T. Coque, F. Baquero, R. Willems, S. Sheppard, E. Feil, W. Hanage, and J. Corander Journal of Theoretical Biology 2016

  @article{Numminen2016,
    author = {Numminen, E. and Gutmann, Michael U and Shubin, Mikhail and Marttinen, Pekka and M\'eric, Guillaume and van Schaik, Willem and Coque, Teresa and Baquero, Fernando and Willems, R.J.L. and Sheppard, S.K. and Feil, E.J. and Hanage, W.P. and Corander, Jukka},
    journal = {Journal of Theoretical Biology},
    title = {The impact of host metapopulation structure on the population genetics of colonizing bacteria},
    year = {2016},
    pages = {53--62},
    volume = {396},
    arxiv = {https://www.biorxiv.org/content/10.1101/027581v1},
    doi = {https://doi.org/10.1016/j.jtbi.2016.02.019},
    keywords = {Bacterial evolution, Genetic structure, Migration, Population dynamics},
    url = {http://www.sciencedirect.com/science/article/pii/S0022519316001156}
  }
  

• Recombination produces coherent bacterial species clusters in both core and accessory genomes P. Marttinen, N. Croucher, M. Gutmann, J. Corander, and W. Hanage Microbial Genomics 2015

  @article{Marttinen2015,
    author = {Marttinen, P. and Croucher, N.J. and Gutmann, M. U. and Corander, J. and Hanage, W.P.},
    journal = {Microbial Genomics},
    title = {Recombination produces coherent bacterial species clusters in both core and accessory genomes},
    year = {2015},
    number = {5},
    volume = {1},
    doi = {http://dx.doi.org/10.1099/mgen.0.000038},
    keywords = {core/accessory genome, evolution, computational modeling, speciation, recombination},
    url = {https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000038}
  }
  

• Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images M. Gutmann, V. Laparra, A. Hyvärinen, and J. Malo PLOS ONE 2014

  @article{Gutmann2014,
    author = {Gutmann, M. U. and Laparra, V. and Hyv\"arinen, A. and Malo, J.},
    journal = {PLOS ONE},
    title = {Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images},
    year = {2014},
    number = {2},
    pages = {e86481},
    volume = {9},
    doi = {https://doi.org/10.1371/journal.pone.0086481},
    keywords = {Generalization of canonical correlation analysis, data fusion, natural image statistics, spatio-chromatic adaptation},
    publisher = {Public Library of Science},
    url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086481}
  }
  

• Direct learning of sparse changes in Markov networks by density ratio estimation S. Liu, J. Quinn, M. Gutmann, T. Suzuki, and M. Sugiyama Neural Computation 2014

  @article{Liu2014,
    author = {Liu, S. and Quinn, J.A. and Gutmann, M. U. and Suzuki, T. and Sugiyama, M.},
    journal = {Neural Computation},
    title = {Direct learning of sparse changes in {M}arkov networks by density ratio estimation},
    year = {2014},
    number = {6},
    pages = {1169--1197},
    volume = {26},
    arxiv = {https://arxiv.org/abs/1304.6803},
    doi = {http://dx.doi.org/10.1162/NECO_a_00589},
    keywords = {Undirected graphical models, density ratio estimation},
    url = {https://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00589}
  }
  

• Estimating dependency structures for non-Gaussian components with linear and energy correlations H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2014

  @inproceedings{Sasaki2014,
    author = {Sasaki, H. and Gutmann, M. U. and Shouno, H. and Hyv\"arinen, A.},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {Estimating dependency structures for non-{G}aussian components with linear and energy correlations},
    year = {2014},
    keywords = {Extensions of ICA, natural image statistics},
    url = {http://proceedings.mlr.press/v33/sasaki14.html}
  }
  

• A three-layer model of natural image statistics M. Gutmann, and A. Hyvärinen Journal of Physiology-Paris 2013

  @article{Gutmann2013,
    author = {Gutmann, M. U. and Hyv\"arinen, A.},
    journal = {Journal of Physiology-Paris},
    title = {A three-layer model of natural image statistics},
    year = {2013},
    number = {5},
    pages = {369--398},
    volume = {107},
    booktitle = {Special issue: Neural Coding and Natural Image Statistics},
    doi = {10.1016/j.jphysparis.2013.01.001},
    issn = {0928-4257},
    keywords = {Natural images, probabilistic modeling, visual processing, selectivity, invariance, sparse coding, unsupervised deep learning},
    url = {http://www.sciencedirect.com/science/article/pii/S0928425713000028}
  }
  

• Estimation of unnormalized statistical models without numerical integration M. Gutmann, and A. Hyvärinen In Proceedings of the Workshop on Information Theoretic Methods in Science and Engineering 2013

  @inproceedings{Gutmann2013b,
    author = {Gutmann, M. U. and Hyv\"arinen, A.},
    booktitle = {Proceedings of the Workshop on Information Theoretic Methods in Science and Engineering},
    title = {Estimation of unnormalized statistical models without numerical integration},
    year = {2013},
    keywords = {Unnormalized models, energy-based models, intractable likelihood},
    url = {http://www.me.inf.kyushu-u.ac.jp/witmse2013/}
  }
  

• Direct learning of sparse changes in Markov networks by density ratio estimation S. Liu, J. Quinn, M. Gutmann, and M. Sugiyama In Machine Learning and Knowledge Discovery in Databases (ECML PKDD) 2013

  @inproceedings{Liu2013,
    author = {Liu, Song and Quinn, JohnA. and Gutmann, Michael U. and Sugiyama, Masashi},
    booktitle = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
    title = {Direct learning of sparse changes in {M}arkov networks by density ratio estimation},
    year = {2013},
    doi = {http://dx.doi.org/10.1007/978-3-642-40991-2_38},
    issn = {978-3-642-40990-5},
    keywords = {Undirected graphical models},
    url = {https://link.springer.com/chapter/10.1007%2F978-3-642-40991-2_38}
  }
  

• Correlated topographic analysis: estimating an ordering of correlated components H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen Machine Learning 2013

  @article{Sasaki2013,
    author = {Sasaki, Hiroaki and Gutmann, Michael U. and Shouno, Hayaru and Hyv\"arinen, Aapo},
    journal = {Machine Learning},
    title = {Correlated topographic analysis: estimating an ordering of correlated components},
    year = {2013},
    number = {2-3},
    pages = {285--317},
    volume = {92},
    doi = {http://dx.doi.org/10.1007/s10994-013-5351-x},
    issn = {0885-6125},
    keywords = {independent component analysis, topographic representation, natural image statistics, higher order features, natural language processing},
    publisher = {Springer US},
    url = {https://link.springer.com/article/10.1007/s10994-013-5351-x}
  }
  

• Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics M. Gutmann, and A. Hyvärinen Journal of Machine Learning Research 2012

  @article{Gutmann2012a,
    author = {Gutmann, M. U. and Hyv\"arinen, A.},
    journal = {Journal of Machine Learning Research},
    title = {{N}oise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics},
    year = {2012},
    pages = {307--361},
    volume = {13},
    keywords = {unnormalised models, unsupervised deep learning, natural image statistics},
    url = {http://www.jmlr.org/papers/v13/gutmann12a.html}
  }
  

• Learning a selectivity–invariance–selectivity feature extraction architecture for images M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Pattern Recognition (ICPR) 2012

  @inproceedings{Gutmann2012b,
    author = {Gutmann, M. U. and Hyv\"arinen, A.},
    booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR)},
    title = {Learning a selectivity--invariance--selectivity feature extraction architecture for images},
    year = {2012},
    keywords = {Natural image statistics,unnormalised model, unsupervised deep learning},
    url = {https://ieeexplore.ieee.org/document/6460284}
  }
  

• Topographic analysis of correlated components H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen In JMLR: Workshop and Conference Proceedings 2012

  @inproceedings{Sasaki2012,
    author = {Sasaki, H. and Gutmann, M. U. and Shouno, H. and Hyv\"arinen, A.},
    booktitle = {JMLR: Workshop and Conference Proceedings},
    title = {Topographic analysis of correlated components},
    year = {2012},
    pages = {1--14},
    series = {Asian Conference on Machine Learning (ACML)},
    volume = {25},
    keywords = {independent component analysis, topographic representation, higher order correlation, linear correlation, natural image statistics, natural language processing.},
    url = {http://proceedings.mlr.press/v25/sasaki12}
  }
  

• Bregman divergence as general framework to estimate unnormalized statistical models M. Gutmann, and J. Hirayama In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 2011

  @inproceedings{Gutmann2011b,
    author = {Gutmann, M. U. and Hirayama, J.},
    booktitle = {Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI)},
    title = {{B}regman divergence as general framework to estimate unnormalized statistical models},
    year = {2011},
    arxiv = {https://arxiv.org/abs/1202.3727},
    keywords = {Unnormalized (energy-based) models, estimation theory, density ratio estimation},
    url = {https://arxiv.org/abs/1202.3727}
  }
  

• Extracting coactivated features from multiple data sets M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Artificial Neural Networks (ICANN) 2011

  @inproceedings{Gutmann2011a,
    author = {Gutmann, M. U. and Hyv{\"a}rinen, A.},
    booktitle = {Proceedings of the International Conference on Artificial Neural Networks (ICANN)},
    title = {{E}xtracting coactivated features from multiple data sets},
    year = {2011},
    doi = {10.1007/978-3-642-21735-7_40},
    keywords = {Data fusion, natural image statistics},
    url = {https://link.springer.com/chapter/10.1007/978-3-642-21735-7_40}
  }
  

• Complex-valued independent component analysis of natural images V. Laparra, M. Gutmann, J. Malo, and A. Hyvärinen In Proceedings of the International Conference on Artificial Neural Networks (ICANN) 2011

  @inproceedings{Laparra2011,
    author = {Laparra, V. and Gutmann, M. U. and Malo, J. and Hyv{\"a}rinen, A.},
    booktitle = {Proceedings of the International Conference on Artificial Neural Networks (ICANN)},
    title = {{C}omplex-valued independent component analysis of natural images},
    year = {2011},
    doi = {10.1007/978-3-642-21738-8_28},
    keywords = {complex independent components analysis, natural image statistics, modeling Fourier phase distribution},
    url = {https://link.springer.com/chapter/10.1007/978-3-642-21738-8_28}
  }
  

• Noise-contrastive estimation: A new estimation principle for unnormalized statistical models M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2010

  @inproceedings{Gutmann2010,
    author = {Gutmann, M. U. and Hyv\"arinen, A.},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {{N}oise-contrastive estimation: {A} new estimation principle for unnormalized statistical models},
    year = {2010},
    address = {Chia Laguna Resort, Sardinia, Italy},
    editor = {Teh, Yee Whye and Titterington, Mike},
    month = {13--15 May},
    pages = {297--304},
    publisher = {JMLR Workshop and Conference Proceedings},
    series = {Proceedings of Machine Learning Research},
    volume = {9},
    keywords = {unnormalized models, energy-based models, estimation theory, natural image statistics},
    url = {http://proceedings.mlr.press/v9/gutmann10a.html}
  }
  

• A family of computationally efficient and simple estimators for unnormalized statistical models M. Pihlaja, M. Gutmann, and A. Hyvärinen In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 2010

  @inproceedings{Pihlaja2010,
    author = {Pihlaja, M. and Gutmann, M. U. and Hyv\"arinen, A.},
    booktitle = {Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI)},
    title = {{A} family of computationally efficient and simple estimators for unnormalized statistical models},
    year = {2010},
    arxiv = {https://arxiv.org/abs/1203.3506},
    keywords = {unnormalized models, energy-based models, estimation theory}
  }
  

• Learning features by contrasting natural images with noise M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Artificial Neural Networks (ICANN) 2009

  @inproceedings{Gutmann2009b,
    author = {Gutmann, M. U. and Hyv{\"a}rinen, A.},
    booktitle = {Proceedings of the International Conference on Artificial Neural Networks (ICANN)},
    title = {{L}earning features by contrasting natural images with noise},
    year = {2009},
    doi = {https://doi.org/10.1007/978-3-642-04277-5_63},
    keywords = {natural image statistics, learning by comparison, unnormalized models, energy-based models},
    url = {https://link.springer.com/chapter/10.1007/978-3-642-04277-5_63}
  }
  

• Learning reconstruction and prediction of natural stimuli by a population of spiking neurons M. Gutmann, and A. Hyvärinen In European Symposium on Artificial Neural Networks (ESANN) 2009

  @inproceedings{Gutmann2009,
    author = {Gutmann, M. U. and Hyv\"arinen, A.},
    booktitle = {European Symposium on Artificial Neural Networks (ESANN)},
    title = {{L}earning reconstruction and prediction of natural stimuli by a population of spiking neurons},
    year = {2009},
    keywords = {Learning data representations, spiking neurons},
    url = {https://www.esann.org/proceedings/2009}
  }
  

• Learning natural image structure with a horizonal product model U. Köster, J. Lindgren, M. Gutmann, and A. Hyvärinen In Proceedings on the International Conference on Independent Component Analysis and Signal Separation 2009

  @inproceedings{Koster2009,
    author = {K\"oster, U. and Lindgren, J. and Gutmann, M. U. and Hyv\"arinen, A.},
    booktitle = {Proceedings on the International Conference on Independent Component Analysis and Signal Separation},
    title = {{L}earning natural image structure with a horizonal product model},
    year = {2009},
    url = {https://link.springer.com/chapter/10.1007/978-3-642-00599-2_64}
  }
  

• Toward data representation with spiking neurons M. Gutmann, and K. Aihara Artificial Life and Robotics 2008

  @article{Gutmann2008,
    author = {Gutmann, M. U. and Aihara, K.},
    journal = {Artificial Life and Robotics},
    title = {{T}oward data representation with spiking neurons},
    year = {2008},
    pages = {223-226},
    volume = {12},
    doi = {10.1007/s10015-007-0471-7},
    keywords = {spiking neuron, encoding, decoding, learning data representation},
    url = {https://link.springer.com/article/10.1007/s10015-007-0471-7}
  }
  

• Learning encoding and decoding filters for data representation with a spiking neuron M. Gutmann, A. Hyvärinen, and K. Aihara In Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2008

  @inproceedings{Gutmann2008c,
    author = {Gutmann, M. and Hyv{\"a}rinen, A. and Aihara, K.},
    booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
    title = {{L}earning encoding and decoding filters for data representation with a spiking neuron},
    year = {2008},
    doi = {https://ieeexplore.ieee.org/document/4633797},
    keywords = {spiking neurons, data representation},
    url = {https://doi.org/10.1109/IJCNN.2008.4633797}
  }
  

• Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2 A. Hyvärinen, M. Gutmann, and P. Hoyer BMC Neuroscience 2005

  @article{Hyvarinen2005,
    author = {Hyv{\"a}rinen, A. and Gutmann, M. and Hoyer, P.O.},
    journal = {BMC Neuroscience},
    title = {{S}tatistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in {V}2},
    year = {2005},
    volume = {6:12},
    doi = {https://doi.org/10.1186/1471-2202-6-12},
    keywords = {Natural image statistics, computational neuroscience, V2},
    url = {https://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-6-12}
  }
  

• Statistical models of images and early vision A. Hyvärinen, P. Hoyer, J. Hurri, and M. Gutmann In Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR) 2005

  @inproceedings{Hyvarinen2005b,
    title = {{S}tatistical models of images and early vision},
    author = {Hyv\"arinen, A. and Hoyer, P.O. and Hurri, J. and Gutmann, M.},
    booktitle = {Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR)},
    year = {2005},
    keywords = {natural image statistics, computational neuroscience},
    url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.3281}
  }
  

Teaching materials

• Pen and Paper Exercises in Machine Learning M. Gutmann University of Edinburgh 2022

  @techreport{Gutmann2022b,
    author = {Gutmann, Michael U.},
    title = {Pen and Paper Exercises in Machine Learning},
    institution = {University of Edinburgh},
    year = {2022},
    arxiv = {https://arxiv.org/abs/2206.13446},
    url = {https://github.com/michaelgutmann/ml-pen-and-paper-exercises}
  }
  

Workshop and other papers

• Bayesian Optimal Experimental Design for Simulator Models of Cognition S. Valentin, S. Kleinegesse, N. Bramley, M. Gutmann, and C. Lucas In NeurIPS 2021 Workshop "AI for Science" 2021

  @inproceedings{Valentin2021,
    author = {Valentin, Simon and Kleinegesse, Steven and Bramley, {Neil R} and Gutmann, {Michael U} and Lucas, {Christopher G}},
    booktitle = {NeurIPS 2021 Workshop {"}AI for Science{"}},
    title = {Bayesian Optimal Experimental Design for Simulator Models of Cognition},
    year = {2021},
    month = oct,
    arxiv = {https://arxiv.org/abs/2110.15632},
    day = {22},
    language = {English},
    url = {https://ai4sciencecommunity.github.io/index.html},
    month_numeric = {10}
  }
  

• Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds S. Kleinegesse, and M. Gutmann arXiv:2105.04379 2021

  @article{Kleinegesse2021a,
    author = {Kleinegesse, Steven and Gutmann, Michael U.},
    journal = {arXiv:2105.04379},
    title = {Gradient-based {B}ayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds},
    year = {2021},
    arxiv = {https://arxiv.org/abs/2105.04379},
    keywords = {Bayesian Experimental Design, Likelihood-Free Inference, Mutual Information, Implicit Models, Parameter Estimation, Model Discrimination},
    url = {https://arxiv.org/abs/2105.04379}
  }
  

• To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions T. Lopez Guevara, R. Pucci, N. Taylor, M. Gutmann, S. Ramamoorthy, and K. Subr In Workshop on Learning and Inference in Robotics: Integrating Structure, Priors and Models 2018

  @inproceedings{LopezGuevara2018,
    author = {Lopez Guevara, Tatiana and Pucci, Rita and Taylor, Nicholas and Gutmann, Michael U. and Ramamoorthy, Subramanian and Subr, Kartic},
    booktitle = {Workshop on Learning and Inference in Robotics: Integrating Structure, Priors and Models},
    title = {To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions},
    year = {2018},
    arxiv = {https://arxiv.org/abs/1904.02431},
    url = {https://drive.google.com/file/d/1qHdn03oZZe6aQiKxEyFTIiy_Y68OzQ1N/view}
  }
  

• Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) T. Dinev, and M. Gutmann arXiv:1810.09899 2018

  @article{Dinev2018,
    author = {Dinev, T. and Gutmann, M.U.},
    journal = {arXiv:1810.09899},
    title = {Dynamic Likelihood-free Inference via Ratio Estimation ({DIRE})},
    year = {2018},
    arxiv = {https://arxiv.org/abs/1810.09899},
    keywords = {likelihood-free inference, approximate Bayesian computation, time-series}
  }
  

• Classification and Bayesian Optimization for Likelihood-Free Inference M. Gutmann, J. Corander, R. Dutta, and S. Kaski arXiv:1502.05503 2015

  @article{Gutmann2015b,
    author = {Gutmann, M.U. and Corander, J. and Dutta, R. and Kaski, S.},
    journal = {arXiv:1502.05503},
    title = {Classification and {B}ayesian Optimization for Likelihood-Free Inference},
    year = {2015},
    arxiv = {https://arxiv.org/abs/1502.05503}
  }
  

• Learning topographic representations for linearly correlated components H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen In Workshop on Deep Learning and Unsupervised Feature Learning, NIPS 2011

  @inproceedings{Sasaki2011,
    author = {Sasaki, H. and Gutmann, M.U. and Shouno, H. and Hyv\"arinen, A.},
    booktitle = {Workshop on Deep Learning and Unsupervised Feature Learning, NIPS},
    title = {{L}earning topographic representations for linearly correlated components},
    year = {2011},
    url = {https://deeplearningworkshopnips2011.wordpress.com/contributions/}
  }
  

• Learning spike-timings based representations of sensory stimuli with leaky integrate-and-fire neurons M. Gutmann, and A. Hyvärinen BMC Neuroscience 2009

  @article{Gutmann2009c,
    author = {Gutmann, Michael and Hyv\"arinen, Aapo},
    journal = {BMC Neuroscience},
    title = {Learning spike-timings based representations of sensory stimuli with leaky integrate-and-fire neurons},
    year = {2009},
    month = jul,
    number = {1},
    pages = {P144},
    volume = {10},
    doi = {https://doi.org/10.1186/1471-2202-10-S1-P144},
    issn = {1471-2202},
    refid = {Gutmann2009},
    url = {https://link.springer.com/article/10.1186/1471-2202-10-S1-P144},
    month_numeric = {7}
  }
  

• Unsupervised learning by discriminating data from artificial noise M. Gutmann, and A. Hyvärinen In NIPS Workshop on Generative Discriminative Learning Interface 2009

  @inproceedings{Gutmann2009d,
    author = {Gutmann, M. and Hyv\"arinen, A.},
    booktitle = {NIPS Workshop on Generative Discriminative Learning Interface},
    title = {{U}nsupervised learning by discriminating data from artificial noise},
    year = {2009},
    url = {http://gen-disc2009.wikidot.com/}
  }