Method interprets GAN latent space via latent variable correlation analysis.
problem Understanding the inner workings of GANs.
method Analyzing correlation between latent variables and semantic contents in generated images.
result A method for controllable semantic content generation in GANs.
We analyze the necessary and sufficient conditions for exact inference of a latent model. In latent models, each entity is associated with a latent variable following some probability distribution. The challenging question we try to solve is: can we perform exact inference without observing the latent variables, even w…
This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.
problem Identifying latent variables in sequential data models.
method Proved identifiability of Markov Switching Models and established conditions for Switching Dynamical Systems.
result Identifiability of latent variables and non-linear mappings in Switching Dynamical Systems up to affine transformations.
Method evaluates disentanglement in DLVMs, including those not aligned with latent axes.
problem Evaluate disentanglement in DLVMs, especially those not aligned with latent axes.
method Proposes a statistical method to discover generative factors of a dataset.
result Empirically demonstrates the advantage of the method on two datasets.
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an "anchor", an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possi…
Study nonparametric factor analysis with arbitrary noise.
problem Identify latent variables in noisy, non-invertible settings.
method Developed a general framework and estimation methods.
result Identify latent variables up to certain indeterminacies.
Enhances interpretability of linear latent spaces through automated clustering and ranking.
problem Severe interpretability issues in latent directions of PCA, ICA, CCA, and FA.
method LS-PIE framework automates clustering and ranking of latent vectors.
result Enhanced interpretability of latent vectors through LR, LS, LC, and LCON.
Causal Component Analysis aims to recover latent variables with causal relationships.
problem Recover latent variables with causal relationships from observed mixtures.
method Introduces a likelihood-based approach using normalizing flows to estimate unmixing function and causal mechanisms.
result Demonstrates effectiveness through synthetic experiments in CauCA and ICA settings.
Hierarchical parametric models consisting of observable and latent variables are widely used for unsupervised learning tasks. For example, a mixture model is a representative hierarchical model for clustering. From the statistical point of view, the models can be regular or singular due to the distribution of data. In …
Hierarchical statistical models are widely employed in information science and data engineering. The models consist of two types of variables: observable variables that represent the given data and latent variables for the unobservable labels. An asymptotic analysis of the models plays an important role in evaluating t…
Hierarchical learning models, such as mixture models and Bayesian networks, are widely employed for unsupervised learning tasks, such as clustering analysis. They consist of observable and hidden variables, which represent the given data and their hidden generation process, respectively. It has been pointed out that co…
Model learns multilingual word representations robust to noise.
problem Learning multilingual word representations in noisy environments.
method Fit a generative latent variable model to a multilingual dictionary.
result Competitive multilingual embeddings across various tasks.
Proposes a method to combine datasets with missing values using Gaussian process latent variables.
problem Combining datasets with missing values under non-Missing at Random (NMAR) missingness.
method Gaussian process latent variable model for non-MAR missing data.
result Valid estimates are obtained using the proposed method, while existing methods provide severely biased estimates.
Deep equilibrium models estimate latent variables from data.
problem Estimating latent variables from data.
method Generalized exponential family models, deep equilibrium networks.
result Deep equilibrium models solve MAP estimates for latent and transformation parameters.
Proposes a transfer learning method for accurate latent variable estimation.
problem Accurate estimation of latent variables in networks with large parameter spaces.
method Leverages information from similar networks to improve estimation accuracy.
result The proposed methods improve estimation accuracy and are validated on real datasets.
Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysi…
New method identifies latent causal variables from observed data, overcoming indeterminacies.
problem Identifying latent causal variables from observed data, especially when latent variables are weight-variant.
method Introduces a novel identifiability condition for latent causal models, proposing SuaVE method.
result Identifies latent causal variables up to trivial permutation and scaling, demonstrating consistency and efficacy.
Latent variable models can be used to probabilistically "fill-in" missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that infers the latent variables given the data variables. However, it is not clear how to handl…
CDVI improves variational inference for survival analysis by considering censoring mechanisms.
problem Challenges in applying variational methods to survival data, especially the dependence on censoring.
method Censor-dependent variational inference (CDVI) tailored for latent variable models in survival analysis.
result Significant improvements in estimating individual survival distributions.
New method identifies latent variables with causal dependencies from observed data.
problem Identify latent variables with causal relationships from observed data.
method Linear causal disentanglement via higher-order cumulants, with perfect and soft interventions.
result Recovery of parameters via coupled tensor decomposition and polynomial equations.
Adaptive probabilistic PCA adapts complexity with varying subspaces.
problem Adaptive probabilistic PCA models varying complexity in data.
method Relaxed linear Gaussian model with discrete latent variables, Bayesian nonparametric approach.
result Proposes locally adaptive probabilistic PCA (A-PPCA) for varying subspaces.
Develops SCMs for latent selection to simplify causal analysis.
problem Latent selection complicates causal analysis.
method Introduces a conditioning operation for SCMs to encode latent selection.
result Conditioning operation preserves simplicity, acyclicity, and linearity of SCMs.
Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervise…
Identifies causal effects in LiNGAM models with latent variables.
problem Identifying causal effects in LiNGAM models with latent confounders.
method Complete graphical characterization and efficient algorithms for certification. RICA adaptation for estimation.
result Efficient algorithms and RICA adaptation for estimating causal effects.
Paper improves variational inference for complex models.
problem Improving statistical accuracy of variational inference in high-dimensional models.
method Developed a general framework for MFVI and proposed a partially grouped VI algorithm.
result Proposed algorithm works and outperforms vanilla MFVI in mixed membership stochastic blockmodel.
Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent varia…
Additive decoders tackle latent variables and image generation.
problem Latent variables identification and out-of-support image generation in representation learning.
method Additive decoders that can identify latent variables up to permutation and block-wise invertible transformations, and generate novel images by recombining observed factors.
result Additive decoders provide a new setting for nonlinear independent component analysis and can generate novel images by recombining observed factors.
New method identifies latent variables in cognitive models using neural networks.
problem Inference of latent variables in complex cognitive models is limited.
method Recurrent neural networks and simulation-based inference for latent variable sequences.
result Extends neural Bayes estimation to broader classes of cognitive models.
Latent variable models are used to estimate variables of interest quantities which are observable only up to some measurement error. In many studies, such variables are known but not precisely quantifiable (such as "job satisfaction" in social sciences and marketing, "analytical ability" in educational testing, or "inf…
Graphical models are commonly used tools for modeling multivariate random variables. While there exist many convenient multivariate distributions such as Gaussian distribution for continuous data, mixed data with the presence of discrete variables or a combination of both continuous and discrete variables poses new cha…
DICCA maps multi-view data into a shared latent space with interpretable components.
problem Learning from multiple related but distinct data views.
method DICCA extends CCA to deep generative networks and uses sparsity-inducing priors for interpretability.
result DICCA effectively disentangles shared and view-specific variations in multi-view data.
This work analyzes VQ-VAEs using information theory, focusing on latent variables and their impact on generalization and data generation.
problem Lack of theoretical analysis for latent variables in unsupervised models like VQ-VAEs.
method Information-theoretic analysis, introducing a novel data-dependent prior.
result Derives a generalization error bound for VQ-VAEs that depends on LV complexity and encoder, not decoder.
D-GCCA improves multi-view data analysis by separating common and distinctive components.
problem Analyzing multi-view high-dimensional data with latent factors.
method Decomposes each view's data matrix into common and distinctive sources with orthogonality constraints.
result Consistent estimators with good performance and efficient computation.
Estimates Granger causality with unobserved confounders using deep latent-variable recurrent neural networks.
problem Non-linear Granger causality with unobserved confounders in observational studies.
method Generative model with latent variable, variational autoencoder, recurrent neural network.
result Estimated confounders improve performance in non-linear Granger causality with multiple proxies.
New latent variable model improves inflation forecasting accuracy.
problem Improving medium-term inflation forecasting accuracy.
method Formulated and tested a latent variable Phillips curve hypothesis using 3,968 factor combinations.
result Latent variable PC models outperform traditional models by 6-8 quarters.
In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which represent the parts of the data that can be directly measured, and latent variables, which represent the underlying processes that generate the d…
This study proposes a method to generate biosignals with controlled characteristics using GANs and latent variable analysis.
problem Unclear relationship between input and generated data from GANs, inability to control generated data characteristics.
method Recurrent GANs with latent variable analysis using CCA to control generated biosignals.
result Effective control of biosignal characteristics using proposed GAN method and latent variable analysis.
We study a norm for structured sparsity which leads to sparse linear predictors whose supports are unions of prede ned overlapping groups of variables. We call the obtained formulation latent group Lasso, since it is based on applying the usual group Lasso penalty on a set of latent variables. A detailed analysis of th…
New method extracts latent variables from process data using autoencoders.
problem Extracting useful information from diverse, noisy, and nonstandard response processes.
method Sequence-to-sequence autoencoder to compress response processes into standard numerical vectors.
result The latent variables extracted from response processes are useful for understanding complex skills.
In the following short article we adapt a new and popular machine learning model for inference on medical data sets. Our method is based on the Variational AutoEncoder (VAE) framework that we adapt to survival analysis on small data sets with missing values. In our model, the true health status appears as a set of late…
Paper shows how SFA fits into FBM framework for time series separation.
problem Identifying time series decomposition in flow-based models.
method Combining SFA and FBM to make time series decomposition identifiable.
result Time series decomposition becomes identifiable using SFA and FBM.
Study identifies parameters in causal models with latent confounding.
problem Parameter identification in linear non-Gaussian causal models with latent confounding.
method Graphical criterion for necessary and sufficient identifiability of direct causal effects, with polynomial-time algorithm.
result Developed a graphical criterion for identifying direct causal effects in latent variable models with arbitrary non-linear confounding.
Proposes flexible auto-encoders for varying data dimensions.
problem Fixed latent dimensions limit data flexibility.
method Stochastic bottleneck with weighted dropouts.
result Seamless variable dimensionality reduction with high performance.
Improved neural network model for predicting latent budgets in compositional data.
problem Predicting response variables in compositional data with non-negativity constraints.
method LBA-NN, a feed forward neural network model that incorporates K-means clustering for interpretation.
result LBA-NN outperforms traditional LBA in prediction accuracy, specificity, recall, and mean square error.
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class and illustrate the usefulness of our sensitivity analysis on real-world datasets. Our method increases the interpretability of complex blac…
The paper reviews identifiability in linear and nonlinear models, from Gaussian to non-Gaussian.
problem Identifiability issues in latent-variable and structural-equation models, especially in nonlinear cases.
method Review of identifiability theory for linear and nonlinear models, including factor analysis and structural equation models.
result Even nonparametric nonlinear models can be estimated with additional assumptions.
New algorithm improves latent variable model estimation.
problem Estimating parameters in latent variable models.
method Jarzynski-adjusted Langevin algorithm (JALA) for SMC methods.
result JALA-EM provides maximum marginal likelihood estimate.
SAE improves VAE's latent space precision in high dimensions.
problem High-dimensional latent codes vs probabilistic inference in VAEs.
method Spherical Auto-Encoder (SAE) with spherical normalization on latent space.
result SAE improves latent code inference precision in high dimensions.