World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
13227
World Ranking
10971
National Ranking
91

Overview

Patrik O. Hoyer is affiliated with the University of Helsinki in Finland. Their research contributions are situated primarily within the field of Computer Science, with notable emphasis on subfields such as Artificial Intelligence and Signal Processing.

Their body of work addresses core topics including Bayesian Modeling and Causal Inference, Advanced Text Analysis Techniques, and Blind Source Separation Techniques. These areas reflect a focus on computational methods that involve statistical modeling, interpretation of complex text data, and extraction of independent signals from data mixtures.

Among Hoyer's recent publications is the paper titled DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model, published in 2021 in UNC Libraries. This work explores a methodological approach relevant to causal inference in linear non-Gaussian settings.

Frequent collaborators in their research include colleagues Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, and Yoshinobu Kawahara. These repeated collaborations indicate ongoing research projects likely focused on related themes in statistical and computational modeling.

  • DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model (2021, UNC Libraries)

  • Shohei Shimizu
  • Takanori Inazumi
  • Yasuhiro Sogawa
  • Aapo Hyvärinen
  • Yoshinobu Kawahara

  • UNC Libraries

  • Computer Science

  • Artificial Intelligence
  • Signal Processing

  • Bayesian Modeling and Causal Inference
  • Advanced Text Analysis Techniques
  • Blind Source Separation Techniques

Best Publications

  • Non-negative Matrix Factorization with Sparseness Constraints

    Patrik O. Hoyer

  • A Linear Non-Gaussian Acyclic Model for Causal Discovery

    Shohei Shimizu;Patrik O. Hoyer;Aapo Hyvärinen;Antti Kerminen

  • Non-negative sparse coding

    P.O. Hoyer

  • Nonlinear causal discovery with additive noise models

    Patrik O. Hoyer;Dominik Janzing;Joris M. Mooij;Jonas Peters

  • Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces

    Aapo Hyvärinen;Patrik Hoyer

  • Topographic Independent Component Analysis

    Aapo Hyvärinen;Patrik O. Hoyer;Mika O. Inki

  • DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model

    Shohei Shimizu;Takanori Inazumi;Yasuhiro Sogawa;Aapo Hyvärinen

  • Independent component analysis applied to feature extraction from colour and stereo images.

    Patrik O Hoyer;Aapo Hyvärinen

  • A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images

    Aapo Hyvärinen;Patrik O. Hoyer

  • Natural Image Statistics

    Aapo Hyvärinen;Jarmo Hurri;Patrik O. Hoyer

  • Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

    Aapo Hyvärinen;Kun Zhang;Shohei Shimizu;Patrik O. Hoyer

  • Causal Inference by Independent Component Analysis: Theory and Applications*

    Alessio Moneta;Doris Entner;Patrik O. Hoyer;Alex Coad

  • Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior

    Patrik O. Hoyer;Aapo Hyvärinen

  • Estimation of causal effects using linear non-Gaussian causal models with hidden variables

    Patrik O. Hoyer;Shohei Shimizu;Antti J. Kerminen;Markus Palviainen

  • A multi-layer sparse coding network learns contour coding from natural images.

    Patrik O Hoyer;Aapo Hyvärinen

  • Modeling receptive fields with non-negative sparse coding

    Patrik O. Hoyer

  • Discovering cyclic causal models by independent components analysis

    Gustavo Lacerda;Peter Spirtes;Joseph Ramsey;Patrik O. Hoyer

  • Image feature extraction by sparse coding and independent component analysis

    A. Hyvarinen;E. Oja;P. Hoyer;J. Hurri

  • Learning linear cyclic causal models with latent variables

    Antti Hyttinen;Frederick Eberhardt;Patrik O. Hoyer

  • Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity

    Aapo Hyvärinen;Shohei Shimizu;Patrik O. Hoyer

  • Experiment selection for causal discovery

    Antti Hyttinen;Frederick Eberhardt;Patrik O. Hoyer

  • Optimization Theory and Algorithms

    Aapo Hyvärinen;Jarmo Hurri;Patrik O. Hoyer

Frequent Co-Authors

Aapo Hyvärinen
Aapo Hyvärinen University of Helsinki
Peter Spirtes
Peter Spirtes Carnegie Mellon University
Dominik Janzing
Dominik Janzing Amazon (United States)
Erkki Oja
Erkki Oja Aalto University
Kenneth A. Bollen
Kenneth A. Bollen University of North Carolina at Chapel Hill
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Richard Scheines
Richard Scheines Carnegie Mellon University
Kun Zhang
Kun Zhang Carnegie Mellon University

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