Search Results for author: Aapo Hyvärinen

Found 27 papers, 12 papers with code

Identifiable Feature Learning for Spatial Data with Nonlinear ICA

no code implementations28 Nov 2023 Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen

In this paper, we introduce a new nonlinear ICA framework that employs $t$-process (TP) latent components which apply naturally to data with higher-dimensional dependency structures, such as spatial and spatio-temporal data.

Disentanglement Variational Inference

Causal Representation Learning Made Identifiable by Grouping of Observational Variables

no code implementations24 Oct 2023 Hiroshi Morioka, Aapo Hyvärinen

A topic of great current interest is Causal Representation Learning (CRL), whose goal is to learn a causal model for hidden features in a data-driven manner.

Causal Discovery Representation Learning

Identifiability of latent-variable and structural-equation models: from linear to nonlinear

no code implementations6 Feb 2023 Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Monti

An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i. e. some parameters cannot be uniquely estimated.

Time Series Time Series Analysis

Painful intelligence: What AI can tell us about human suffering

no code implementations27 May 2022 Aapo Hyvärinen

Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which is why AI can be used as a model of the human brain and mind.


Binary Independent Component Analysis: A Non-stationarity-based Approach

1 code implementation30 Nov 2021 Antti Hyttinen, Vitória Barin-Pacela, Aapo Hyvärinen

Experiments give insight into the requirements for the number of observed variables, segments, and latent sources that allow the model to be estimated.


Shared Independent Component Analysis for Multi-Subject Neuroimaging

1 code implementation NeurIPS 2021 Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen

While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.


Adaptive Multi-View ICA: Estimation of noise levels for optimal inference

no code implementations22 Feb 2021 Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion

By contrast, we propose Adaptive multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources.


Causal Autoregressive Flows

2 code implementations4 Nov 2020 Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen

We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions.

Causal Discovery Causal Inference +1

Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series

1 code implementation22 Jun 2020 Hermanni Hälvä, Aapo Hyvärinen

The central idea in such works is that the latent components are assumed to be independent conditional on some observed auxiliary variables, such as the time-segment index.

Disentanglement Time Series +1

Independent Innovation Analysis for Nonlinear Vector Autoregressive Process

no code implementations19 Jun 2020 Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen

Additivity greatly limits the generality of the model, hindering analysis of general NVAR processes which have nonlinear interactions between the innovations.

Time Series Time Series Analysis

Modeling Shared Responses in Neuroimaging Studies through MultiView ICA

1 code implementation NeurIPS 2020 Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin

Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.


ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA

1 code implementation NeurIPS 2020 Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen

We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional energy-based models are unique in function space, up to a simple transformation.

Transfer Learning

Robust contrastive learning and nonlinear ICA in the presence of outliers

no code implementations1 Nov 2019 Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti, Aapo Hyvärinen

We develop two robust nonlinear ICA methods based on the {\gamma}-divergence, which is a robust alternative to the KL-divergence in logistic regression.

Causal Discovery Contrastive Learning +2

Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs

no code implementations22 Jul 2019 Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama

On the other hand, compressing the vertices while preserving the directed edge information provides a way to learn the small-scale representation of a directed graph.


Variational Autoencoders and Nonlinear ICA: A Unifying Framework

2 code implementations10 Jul 2019 Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen

We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement.


Neural-Kernelized Conditional Density Estimation

no code implementations5 Jun 2018 Hiroaki Sasaki, Aapo Hyvärinen

Among existing methods, non-parametric and/or kernel-based methods are often difficult to use on large datasets, while methods based on neural networks usually make restrictive parametric assumptions on the probability densities.

Density Estimation Dimensionality Reduction +1

A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data

no code implementations24 May 2018 Ricardo Pio Monti, Aapo Hyvärinen

We propose a probabilistic model which simultaneously performs both a grouping of variables (i. e., detecting community structure) and estimation of connectivities between the groups which correspond to latent variables.

Connectivity Estimation

Deep Energy Estimator Networks

1 code implementation21 May 2018 Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen

We present the utility of DEEN in learning the energy, the score function, and in single-step denoising experiments for synthetic and high-dimensional data.

Denoising Density Estimation

SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling

no code implementations ICML 2017 Jun-Ichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe

We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling.


Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios

no code implementations6 Jul 2017 Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama

Based on the proposed estimator, novel methods both for mode-seeking clustering and density ridge estimation are developed, and the respective convergence rates to the mode and ridge of the underlying density are also established.

Clustering Density Estimation

Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure

no code implementations18 Jun 2015 Hiroaki Sasaki, Michael U. Gutmann, Hayaru Shouno, Aapo Hyvärinen

The precision matrix of the linear components is assumed to be randomly generated by a higher-order process and explicitly parametrized by a parameter matrix.

Density Estimation in Infinite Dimensional Exponential Families

1 code implementation12 Dec 2013 Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

When $p_0\in\mathcal{P}$, we show that the proposed estimator is consistent, and provide a convergence rate of $n^{-\min\left\{\frac{2}{3},\frac{2\beta+1}{2\beta+2}\right\}}$ in Fisher divergence under the smoothness assumption that $\log p_0\in\mathcal{R}(C^\beta)$ for some $\beta\ge 0$, where $C$ is a certain Hilbert-Schmidt operator on $H$ and $\mathcal{R}(C^\beta)$ denotes the image of $C^\beta$.

Density Estimation

Structural equations and divisive normalization for energy-dependent component analysis

no code implementations NeurIPS 2011 Jun-Ichiro Hirayama, Aapo Hyvärinen

Here, we propose a principled probabilistic model to model the energy- correlations between the latent variables.

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