A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Bayesian approach learns nonparametric mixture components from heterogeneous data.
We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable corresponding to the mixture components is hidden and each mixture component over the observed variables can have a potentially different Markov graph structure and parameters. We propose a novel approach for estimati…
When estimating finite mixture models, it is common to make assumptions on the mixture components, such as parametric assumptions. In this work, we make no distributional assumptions on the mixture components and instead assume that observations from the mixture model are grouped, such that observations in the same gro…
New bounds on sample size for identifying mixture models with grouped samples.
Mixture components improve VAE performance by increasing latent flexibility.
Identifying components and estimating mixing weights in unlabeled finite mixtures under marginal independence.
Enhances mixture models with classifier-defined weights.
In many applications, a finite mixture is a natural model, but it can be difficult to choose an appropriate number of components. To circumvent this choice, investigators are increasingly turning to Dirichlet process mixtures (DPMs), and Pitman-Yor process mixtures (PYMs), more generally. While these models may be well…
Study identifies components of unknown interventions in a mixture.
Finite mixture models are statistical models which appear in many problems in statistics and machine learning. In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures. When estimating …
A new EM gradient algorithm for mixture models with skewed components.
New method reduces mixture model evaluation cost for large models.
Algorithm estimates nonparametric mixtures from grouped data.
Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function, and using the top eigenvectors of the normalized Laplacian to recover the connected components. We study the performance of spectral clust…
Continuous-time interpolation of volatility surfaces preserving mixtures and arbitrage-free.
Algorithm distinguishes Gaussian mixtures from pure Gaussians in quasi-polynomial time.
New method estimates mixture model components efficiently.
Study reveals structure of local minima in GMMs, identifying key cluster centers.
We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such mixture distributions and prove that this representation encodes the conditional independence relations of the mixture distribution. We then…
Study uniform rates for estimating Gaussian mixtures without separation assumption.
Essential principal components simplify spectral analysis with minimal training data.
This work provides a computationally efficient and statistically consistent moment-based estimator for mixtures of spherical Gaussians. Under the condition that component means are in general position, a simple spectral decomposition technique yields consistent parameter estimates from low-order observable moments, wit…
New method identifies shared components from unpaired multimodal mixtures.
We study the problem of learning a mixture model of non-parametric product distributions. The problem of learning a mixture model is that of finding the component distributions along with the mixing weights using observed samples generated from the mixture. The problem is well-studied in the parametric setting, i.e., w…
Detecting and recovering labels in binomial logistic mixtures is challenging due to an information gap.
A fast method estimates Gaussian mixture components without iterative fitting.
Proposes a new model for mixed membership in Gaussian mixture.
The expectation-maximization (EM) algorithm has been widely used in minimizing the negative log likelihood (also known as cross entropy) of mixture models. However, little is understood about the goodness of the fixed points it converges to. In this paper, we study the regions where one component is missing in two-comp…
The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a kn…
Humans perceive the seemingly chaotic world in a structured and compositional way with the prerequisite of being able to segregate conceptual entities from the complex visual scenes. The mechanism of grouping basic visual elements of scenes into conceptual entities is termed as perceptual grouping. In this work, we pro…
New method identifies latent components in PNL mixtures without strong assumptions.
Improves scalability and efficiency of mixture models in black-box variational inference.
Improved convergence rates for MLE in mixture models using penalized log-likelihood.
New method selects FMM components via variational Bayes.
Understanding separation effects on parameter estimation in finite Gaussian mixtures
We introduce a balloon estimator in a generalized expectation-maximization method for estimating all parameters of a Gaussian mixture model given one data sample per mixture component. Instead of limiting explicitly the model size, this regularization strategy yields low-complexity sparse models where the number of eff…
HeMPPCAT improves PCA for data with varying noise.
The efficacy of family-based approaches to mixture model-based clustering and classification depends on the selection of parsimonious models. Current wisdom suggests the Bayesian information criterion (BIC) for mixture model selection. However, the BIC has well-known limitations, including a tendency to overestimate th…
A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data. By assuming common component factor loadings, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well…
We consider the problem of learning a mixture of Random Utility Models (RUMs). Despite the success of RUMs in various domains and the versatility of mixture RUMs to capture the heterogeneity in preferences, there has been only limited progress in learning a mixture of RUMs from partial data such as pairwise comparisons…
A new method detects changes in mixture models quickly and accurately.
Paper proposes a new method for estimating mixture proportions without irreducibility assumption.
We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically, within day and from day to day. The second shortcoming is the wide-spread use of Gaussian probability dens…
This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of th…
New algorithm improves mixing in Bayesian mixture models.
The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high d…
Algorithm learns Gaussian mixtures robust to outliers.
This paper studies clustering and embedding in high-dimensional Gaussian mixture block models.