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.
Study calculates tail risk for various mixture distributions.
Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, we focus on mixture distributions that arise naturally in several application domains where the data contains different sub-populations. For …
Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian -stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that e…
Consistent estimator for mixtures of nonparametric elliptical distributions helps cluster analysis.
Paper proposes a new Wasserstein distance for mixtures of radially contoured distributions.
NMDR estimates complex mixtures of distributions efficiently.
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…
Deep neural networks converge to Gaussian mixtures as layer width increases.
The paper examines risk aggregation under mixtures of marginals, finding that more homogeneous distributions lead to larger uncertainty.
Optimal transport for vector Gaussian mixtures improves efficiency and structure preservation.
A new method uses Mean Field Games to optimize mixture models of Bernoulli and categorical distributions.
Mixture modelling involves explaining some observed evidence using a combination of probability distributions. The crux of the problem is the inference of an optimal number of mixture components and their corresponding parameters. This paper discusses unsupervised learning of mixture models using the Bayesian Minimum M…
Proposes a new model for clustering with heavier tails.
Langevin Dynamics fails to sample from mixture distributions efficiently.
We derive relations between theoretical properties of restricted Boltzmann machines (RBMs), popular machine learning models which form the building blocks of deep learning models, and several natural notions from discrete mathematics and convex geometry. We give implications and equivalences relating RBM-representable …
Bayesian approach learns nonparametric mixture components from heterogeneous data.
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…
We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixtures with shared variance, binomial mixtures with shared success probability, and Poisson mixtures, among others. An example result is that …
This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required background is briefly reviewed before explaining the main algorithm. In explaining the main algorithm, first, fitting a mixture of two distribu…
The mixture of Gaussian distributions, a soft version of k-means , is considered a state-of-the-art clustering algorithm. It is widely used in computer vision for selecting classes, e.g., color, texture, and shapes. In this algorithm, each class is described by a Gaussian distribution, defined by its mean and covarianc…
New insights into identifying mixtures of product distributions using Hadamard extensions.
New EM algorithm for mixtures of elliptical distributions handles missing data and outliers.
In this paper, we generalize the parametric Delta-VaR methods from portfolios with elliptic distributed risk factors to portfolios with mixture of elliptically distributed ones. We treat both the Expected Shortfall and the Value-at-Risk of such portfolios. Special attention is given to the particular case of the mixtur…
SMT trains generative models by estimating mixture scores, outperforming existing methods.
SFSVI uses Gaussian mixtures to approximate neural network outputs for continual learning.
Refined analysis of Mitra's algorithm for discrete mixtures.
MixTS uses a mixture prior to analyze Thompson Sampling in multi-task learning.
Paper establishes universal lower bounds and optimal rates for clustering sub-exponential mixture models.
The paper proposes a method for interpretable mixture density estimation using a tree structure.
This paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models, using transportation distances (i.e., Wasserstein metrics). The relationship between Wasserstein distances on the space of mixing measures and f-divergence functionals such as Hellinger and Kullback-Leibl…
Model-based clustering imposes a finite mixture modelling structure on data for clustering. Finite mixture models assume that the population is a convex combination of a finite number of densities, the distribution within each population is a basic assumption of each particular model. Among all distributions that have …
Improved VAE estimation from incomplete data using variational mixtures.
Algorithm estimates nonparametric mixtures from grouped data.
Algorithm identifies sources in product distributions with improved complexity.
We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs from all the individual training distributions. This covariate shift is caused, in part, due to unobserved features in the datasets. The objec…
The study applies spatial density models to mobile node movements using Möbius distributions.
The modelling of empirically observed data is commonly done using mixtures of probability distributions. In order to model angular data, directional probability distributions such as the bivariate von Mises (BVM) is typically used. The critical task involved in mixture modelling is to determine the optimal number of co…
A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the general inverse Gaussian distribution. This approach is mathematically elegant and relatively comput…
New sampling and identity-testing methods for mixtures of distributions that don't satisfy approximate tensorization of entropy.
New theorem improves spectral gap for sampling from mixture distributions.
Improved VB algorithm for NIG mixtures outperforms Gaussian mixtures for non-Gaussian data.
Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction. In this work, we propose a new framework for variance reduction that enables the use of mixtures over predefined sampling distributions, which can naturally encode prior knowledg…
The thesis models financial returns using mixtures of generalized normal distributions.
New framework optimizes label shift adaptation using aligned distribution mixture.
We study the problem of separating a mixture of distributions, all of which come from interventions on a known causal bayesian network. Given oracle access to marginals of all distributions resulting from interventions on the network, and estimates of marginals from the mixture distribution, we want to recover the mixi…
Efficient algorithm learns mixture models of heavy-tailed distributions.
Diffusion models accurately recover mixture weights from generated samples despite score function insensitivity.
New Gamma-Poisson model improves topic selection for short text.