
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
We introduce a new general identifiable framework for principled disenta...
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Causal Autoregressive Flows
Two apparently unrelated fields – normalizing flows and causality – have...
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Uncovering the structure of clinical EEG signals with selfsupervised learning
Objective. Supervised learning paradigms are often limited by the amount...
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Autoregressive flowbased causal discovery and inference
We posit that autoregressive flow models are wellsuited to performing a...
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Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data i...
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Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
Recent advances in nonlinear Independent Component Analysis (ICA) provid...
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Independent innovation analysis for nonlinear vector autoregressive process
The nonlinear vector autoregressive (NVAR) model provides an appealing f...
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ICEBeeM: Identifiable Conditional EnergyBased Deep Models
Despite the growing popularity of energybased models, their identifiabi...
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Selfsupervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost  and sometimes ...
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Robust contrastive learning and nonlinear ICA in the presence of outliers
Nonlinear independent component analysis (ICA) is a general framework fo...
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Interpretable brain age prediction using linear latent variable models of functional connectivity
Neuroimagingdriven prediction of brain age, defined as the predicted bi...
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Direction Matters: On InfluencePreserving Graph Summarization and Maxcut Principle for Directed Graphs
Summarizing largescaled directed graphs into smallscale representation...
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Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn...
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Information criteria for nonnormalized models
Many statistical models are given in the form of nonnormalized densitie...
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Causal Discovery with General NonLinear Relationships Using NonLinear ICA
We consider the problem of inferring causal relationships between two or...
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Neural Empirical Bayes
We formulate a novel framework that unifies kernel density estimation an...
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NeuralKernelized Conditional Density Estimation
Conditional density estimation is a general framework for solving variou...
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A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from HighDimensional Data
Connectivity estimation is challenging in the context of highdimensiona...
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Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Nonlinear ICA is a fundamental problem for unsupervised representation l...
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Deep Energy Estimator Networks
Density estimation is a fundamental problem in statistical learning. Thi...
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Estimation of NonNormalized Mixture Models and Clustering Using Deep Representation
We develop a general method for estimating a finite mixture of nonnorma...
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ModeSeeking Clustering and Density Ridge Estimation via Direct Estimation of DensityDerivativeRatios
Modes and ridges of the probability density function behind observed dat...
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Unsupervised Feature Extraction by TimeContrastive Learning and Nonlinear ICA
Nonlinear independent component analysis (ICA) provides an appealing fra...
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Simultaneous Estimation of NonGaussian Components and their Correlation Structure
The statistical dependencies which independent component analysis (ICA) ...
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A direct method for estimating a causal ordering in a linear nonGaussian acyclic model
Structural equation models and Bayesian networks have been widely used t...
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Clustering via Mode Seeking by Direct Estimation of the Gradient of a LogDensity
Mean shift clustering finds the modes of the data probability density by...
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Density Estimation in Infinite Dimensional Exponential Families
In this paper, we consider an infinite dimensional exponential family, P...
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Bridging Information Criteria and Parameter Shrinkage for Model Selection
Model selection based on classical information criteria, such as BIC, is...
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ParceLiNGAM: A causal ordering method robust against latent confounders
We consider learning a causal ordering of variables in a linear nonGaus...
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Discovery of nongaussian linear causal models using ICA
In recent years, several methods have been proposed for the discovery of...
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Causal discovery of linear acyclic models with arbitrary distributions
An important task in data analysis is the discovery of causal relationsh...
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On the Identifiability of the PostNonlinear Causal Model
By taking into account the nonlinear effect of the cause, the inner nois...
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Estimation of causal orders in a linear nonGaussian acyclic model: a method robust against latent confounders
We consider to learn a causal ordering of variables in a linear nonGaus...
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Source Separation and HigherOrder Causal Analysis of MEG and EEG
Separation of the sources and analysis of their connectivity have been a...
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A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models
We introduce a new family of estimators for unnormalized statistical mod...
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DirectLiNGAM: A direct method for learning a linear nonGaussian structural equation model
Structural equation models and Bayesian networks have been widely used t...
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Aapo Hyvärinen
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Professor of Machine Learning at University College London