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Found 30 papers, 13 papers with code
Identifiable Multi-View Causal Discovery Without Non-Gaussianity
We propose a novel approach to linear causal discovery in the framework of multi-view Structural Equation Models (SEM).
Density Ratio Estimation with Conditional Probability Paths
Density ratio estimation in high dimensions can be reframed as integrating a certain quantity, the time score, over probability paths which interpolate between the two densities.
A noise-corrected Langevin algorithm and sampling by half-denoising
The Langevin algorithm is a classic method for sampling from a given pdf in a real space.
Identifiable Feature Learning for Spatial Data with Nonlinear ICA
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.
Causal Representation Learning Made Identifiable by Grouping of Observational Variables
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.
Identifiability of latent-variable and structural-equation models: from linear to nonlinear
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i. e. some parameters cannot be uniquely estimated.
Painful intelligence: What AI can tell us about human suffering
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
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
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
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
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.
Uncovering the structure of clinical EEG signals with self-supervised learning
Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
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.
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process
Additivity greatly limits the generality of the model, hindering analysis of general NVAR processes which have nonlinear interactions between the innovations.
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
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
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.
Self-supervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains.
Robust contrastive learning and nonlinear ICA in the presence of outliers
We develop two robust nonlinear ICA methods based on the {\gamma}-divergence, which is a robust alternative to the KL-divergence in logistic regression.
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
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
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
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.
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
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.
Deep Energy Estimator Networks
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.
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling
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
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.
Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
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.
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density
We then develop a mean-shift-like fixed-point algorithm to find the modes of the density for clustering.
Density Estimation in Infinite Dimensional Exponential Families
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 $C^\beta$.
Structural equations and divisive normalization for energy-dependent component analysis
Here, we propose a principled probabilistic model to model the energy- correlations between the latent variables.
