arXiv research

Improved VAE estimation from incomplete data using variational mixtures.

New concept of mixture complexity helps detect gradual clustering changes.

Two approaches improve parameter learning in various mixture models.

NMDR estimates complex mixtures of distributions efficiently.

Enhances mixture models with classifier-defined weights.

Improved sample complexity for Gaussian Mixture Models using Pair Correlation Factor.

NPMLE estimator automatically chooses the right model complexity for Gaussian mixtures.

Efficient algorithms for sparse parameter recovery in mixture models.

A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mi…

Algorithm identifies sources in product distributions with improved complexity.

Improved sample efficiency for private learning of Gaussian mixtures.

New algorithm learns permutations mixtures with optimal sample complexity.

Estimates MLDS using tensor decomposition, improving upon existing methods.

DGMEs use Gaussian mixtures to quantify uncertainty in deep learning.

Paper detects gradual changes in cluster structure using MC fusion.

We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning. Our infinite mixture prototypes represent each class by a set of clusters, unlike existing prototypical methods that represent each class by a single cluster. By inferring the number of clust…

This paper studies generalization in machine learning with mixture data.

This paper optimizes Gaussian mixture model learning with optimal sampling complexity.

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…

Spatially constrained Gaussian mixture models reduce covariance complexity.

We propose two neural network based mixture models in this article. The proposed mixture models are explicit in nature. The explicit models have analytical forms with the advantages of computing likelihood and efficiency of generating samples. Computation of likelihood is an important aspect of our models. Expectation-…

Bayesian models combine experts with a flexible gating mechanism for complex 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…

Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are considered. The use of variational Bayes approximations here is a substantial departure fr…

Graph Neural Networks struggle with generalization, especially OOD data; GRATIN solves this with Gaussian Mixture Model-based augmentation.

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…

New SQ lower bounds show learning mixtures of bounded covariance Gaussians is hard.

Understanding separation effects on parameter estimation in finite Gaussian mixtures

Langevin Dynamics fails to sample from mixture distributions efficiently.

The paper proposes a method for interpretable mixture density estimation using a tree structure.

The paper develops methods to create reliable prediction sets for complex mixture models in high-dimensional data.

Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations. The current practice for estimating the parameters of such models relies on local search heuristics (e.g., the EM algorithm) which are prone to failure, and existing consistent methods …

Bayesian approach learns nonparametric mixture components from heterogeneous data.

Solves challenges in estimating parameters of softmax gating Gaussian mixture models.

This paper tackles causal interactions in mixtures of DAGs using interventions.

New method for summarizing Bayesian mixture models using sliced Wasserstein distances.

EM algorithm achieves optimal sample complexity for well-separated Gaussian mixtures.

New method estimates mixture model components efficiently.

A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent dimensions. The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks. In this paper, we analyze the clusterability of BMMs from…

Improved sample and time complexity for identifying mixtures of product distributions.

Efficient private algorithms for estimating block models and mixture models.

Improved Gaussian process experts model for complex data.

We consider the problem of identifying the parameters of an unknown mixture of two arbitrary $d$-dimensional gaussians from a sequence of independent random samples. Our main results are upper and lower bounds giving a computationally efficient moment-based estimator with an optimal convergence rate, thus resolving a p…

We present new algorithms for detecting the emergence of a community in large networks from sequential observations. The networks are modeled using Erdos-Renyi random graphs with edges forming between nodes in the community with higher probability. Based on statistical changepoint detection methodology, we develop thre…

In this paper, we study the modeling and the classification of functional data presenting regime changes over time. We propose a new model-based functional mixture discriminant analysis approach based on a specific hidden process regression model that governs the regime changes over time. Our approach is particularly a…

New method reduces mixture model evaluation cost for large models.

Study proposes a new metric for comparing Gaussian mixtures in RKHS.

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…