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03/29/2023
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning
A central problem in unsupervised deep learning is how to find useful re...
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02/06/2023
Identifiability of latent-variable and structural-equation models: from linear to nonlinear
An old problem in multivariate statistics is that linear Gaussian models...
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01/23/2023
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Self-supervised learning is an increasingly popular approach to unsuperv...
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05/27/2022
Painful intelligence: What AI can tell us about human suffering
This book uses the modern theory of artificial intelligence (AI) to unde...
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03/02/2022
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think
Learning a parametric model of a data distribution is a well-known stati...
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11/30/2021
Binary Independent Component Analysis via Non-stationarity
We consider independent component analysis of binary data. While fundame...
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06/17/2021
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
We introduce a new general identifiable framework for principled disenta...
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11/04/2020
Causal Autoregressive Flows
Two apparently unrelated fields – normalizing flows and causality – have...
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07/31/2020
Uncovering the structure of clinical EEG signals with self-supervised learning
Objective. Supervised learning paradigms are often limited by the amount...
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07/18/2020
Autoregressive flow-based causal discovery and inference
We posit that autoregressive flow models are well-suited to performing a...
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06/26/2020
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data i...
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06/22/2020
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
Recent advances in nonlinear Independent Component Analysis (ICA) provid...
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06/19/2020
Independent innovation analysis for nonlinear vector autoregressive process
The nonlinear vector autoregressive (NVAR) model provides an appealing f...
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02/26/2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models
Despite the growing popularity of energy-based models, their identifiabi...
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11/13/2019
Self-supervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost - and sometimes ...
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11/01/2019
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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08/05/2019
Interpretable brain age prediction using linear latent variable models of functional connectivity
Neuroimaging-driven prediction of brain age, defined as the predicted bi...
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07/22/2019
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
Summarizing large-scaled directed graphs into small-scale representation...
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07/10/2019
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn...
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05/15/2019
Information criteria for non-normalized models
Many statistical models are given in the form of non-normalized densitie...
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04/19/2019
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
We consider the problem of inferring causal relationships between two or...
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03/06/2019
Neural Empirical Bayes
We formulate a novel framework that unifies kernel density estimation an...
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06/05/2018
Neural-Kernelized Conditional Density Estimation
Conditional density estimation is a general framework for solving variou...
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05/24/2018
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
Connectivity estimation is challenging in the context of high-dimensiona...
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05/22/2018
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Nonlinear ICA is a fundamental problem for unsupervised representation l...
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05/21/2018
Deep Energy Estimator Networks
Density estimation is a fundamental problem in statistical learning. Thi...
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05/19/2018
Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation
We develop a general method for estimating a finite mixture of non-norma...
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07/06/2017
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Modes and ridges of the probability density function behind observed dat...
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05/20/2016
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Nonlinear independent component analysis (ICA) provides an appealing fra...
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06/18/2015
Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
The statistical dependencies which independent component analysis (ICA) ...
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08/09/2014
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Structural equation models and Bayesian networks have been widely used t...
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04/20/2014
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density
Mean shift clustering finds the modes of the data probability density by...
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12/12/2013
Density Estimation in Infinite Dimensional Exponential Families
In this paper, we consider an infinite dimensional exponential family, P...
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07/08/2013
Bridging Information Criteria and Parameter Shrinkage for Model Selection
Model selection based on classical information criteria, such as BIC, is...
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03/29/2013
ParceLiNGAM: A causal ordering method robust against latent confounders
We consider learning a causal ordering of variables in a linear non-Gaus...
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07/04/2012
Discovery of non-gaussian linear causal models using ICA
In recent years, several methods have been proposed for the discovery of...
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06/13/2012
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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05/09/2012
On the Identifiability of the Post-Nonlinear Causal Model
By taking into account the nonlinear effect of the cause, the inner nois...
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04/09/2012
Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders
We consider to learn a causal ordering of variables in a linear non-Gaus...
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03/15/2012
Source Separation and Higher-Order Causal Analysis of MEG and EEG
Separation of the sources and analysis of their connectivity have been a...
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03/15/2012
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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01/13/2011
