arXiv research

Discover causal structure from mixtures of DAGs using latent variable algorithms.

Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel m…

A new neural sequence prediction method using error-correcting codes improves accuracy and convergence.

New method uses subtractive mixture models for approximate inference.

Improves VAEs for generating data from mixed distributions.

Paper presents a reparameterized DP-DLGMM for clustering.

Study reconstructs causal graph from latent variables using mixture oracles.

We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space can exceed the observed dimensionality. In particular, we consider multiview mixtures, spherical Gaussian mixtures, ICA, and sparse coding models. We provide tight concentration…

Two neural network-based mixture models with E-M learning for efficient likelihood computation.

A new VIS approach improves log-likelihood estimation in latent variable models.

Method identifies latent variables from high-dimensional data with piecewise affine mixing.

Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets. They present, however, subtleties in training often manifesting in the discrete latent being under leveraged. In this paper, we show that such models are more amenable to training whe…

We present a novel analysis of the dynamics of tensor power iterations in the overcomplete regime where the tensor CP rank is larger than the input dimension. Finding the CP decomposition of an overcomplete tensor is NP-hard in general. We consider the case where the tensor components are randomly drawn, and show that …

Model disentangles timbre and pitch for musical instruments.

Study learns Gaussian mixtures from censored data.

Corrected whitening restores orthogonality in high-dimensional spherical Gaussian mixtures.

Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not re…

Novel connections between Neyman-Scott processes and Bayesian nonparametric mixture models enable scalable inference.

A new method for categorical variational inference using discrete normalizing flows.

Framework LiLY recovers latent causal variables from time-series data under distribution shifts.

This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating…

The paper improves EM for clustering in a mixture of linear regression models.

LEAP identifies latent causal variables from temporal data.

This work considers the problem of learning the structure of multivariate linear tree models, which include a variety of directed tree graphical models with continuous, discrete, and mixed latent variables such as linear-Gaussian models, hidden Markov models, Gaussian mixture models, and Markov evolutionary trees. The …

We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns models over small groups of variables and iteratively merges onto a global solution. The structure learning involves combinatorial operations…

VampPrior Mixture Model improves clustering in DLVMs.

StepMix estimates mixture models with covariates for social science applications.

An efficient way to learn deep density models that have many layers of latent variables is to learn one layer at a time using a model that has only one layer of latent variables. After learning each layer, samples from the posterior distributions for that layer are used as training data for learning the next layer. Thi…

Proposes a new prior for deep generative models to capture latent properties.

Concentration of infinitely exchangeable sequences with bounded-difference constants

RAD approach models both continuous and discrete data.

A new metric assesses latent variable models using data and model moments.

New method identifies latent sources from nonlinear mixtures without auxiliary variables.

We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models. Collapsed Gibbs is a popular MCMC method, which integrates out variables in the posterior to improve mixing. Unfortunately for many complex models, integrating out these variables is either analytically or co…

Proposes an exact slice sampler for HDP and its mixture models.

Latent MoS learns multiple symmetries for efficient dynamic learning.

VLAC clusters data hierarchically, outperforming GMM.

Finite mixture models have become a popular tool for clustering. Amongst other uses, they have been applied for clustering longitudinal data and clustering high-dimensional data. In the latter case, a latent Gaussian mixture model is sometimes used. Although there has been much work on clustering using latent variables…

Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima. In this paper, we develop a new computationally efficient and provably consistent estimator for a mixture of linear regressions, a simple instance of a discriminative latent-variable mode…

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, wh…

There is a widespread need for statistical methods that can analyze high-dimensional datasets with- out imposing restrictive or opaque modeling assumptions. This paper describes a domain-general data analysis method called CrossCat. CrossCat infers multiple non-overlapping views of the data, each consisting of a subset…

Mixture components improve VAE performance by increasing latent flexibility.

Bayesian active learning reduces data needed for latent variable models.

A new method detects changes in mixture models quickly and accurately.

Model detects epileptic seizures in EEG with high sensitivity.

New method classifies mixtures without labels, recovering latent classes.

Algorithm learns multiple LDS models from unlabeled short trajectories.

New method for learning multidimensional CDFs using Archimedean copulas.