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.
Optimal transport for vector Gaussian mixtures improves efficiency and structure preservation.
Gaussian processes adapted for Riemannian manifolds using gauge-independent kernels.
The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.
The paper introduces novel Gaussian process models for vector-valued signals on manifolds.
New method estimates Gaussian vector functions more efficiently.
Generalizes randomized SVD for better matrix approximations using Gaussian vectors.
Gaussian random vectors exhibit the loss of dimension phenomena, which relate to their joint survival tail behaviour. Besides, the fact that the components of such vectors are light-tailed complicates the approximations of various multivariate risk measures significantly. In this contribution we derive precise approxim…
VOGP efficiently identifies Pareto optimal solutions in black-box vector optimization.
Stochastic trace estimation with tensor train random vectors
Bayesian approach approximates probability functions of Gaussian mixtures.
Study improves error bounds for sparse regression with heavy-tailed covariates.
Study improves self-normalized bounds for vector-valued processes beyond sub-Gaussianity.
We describe various sets of conditional independence relationships, sufficient for qualitatively comparing non-vanishing squared partial correlations of a Gaussian random vector. These sufficient conditions are satisfied by several graphical Markov models. Rules for comparing degree of association among the vertices of…
This work develops discrete Gaussian models for vector-valued data on triangular meshes.
Study on overlaps of singular vectors in Gaussian matrix submatrices.
Bayesian ODEs with Gaussian processes infer unknown dynamics from data.
Andreas Maurer in the paper "A vector-contraction inequality for Rademacher complexities" extended the contraction inequality for Rademacher averages to Lipschitz functions with vector-valued domains; He did it replacing the Rademacher variables in the bounding expression by arbitrary idd symmetric and sub-gaussian var…
Improved spectral method recovers sparse vectors in random subspaces.
Gaussianization flows transform any random vector into a Gaussian, enabling efficient computation and sample generation.
The support vector clustering algorithm is a well-known clustering algorithm based on support vector machines using Gaussian or polynomial kernels. The classical support vector clustering algorithm works well in general, but its performance degrades when applied on big data. In this paper, we have investigated the perf…
Improved vector quantization using Gaussian mixtures for better codebook utilization.
New framework models complex spatial data with basis functions and graphical vectors.
We introduce a new functional measure of tail dependence for weakly dependent (asymptotically independent) random vectors, termed weak tail dependence function. The new measure is defined at the level of copulas and we compute it for several copula families such as the Gaussian copula, copulas of a class of Gaussian mi…
This paper studies clustering and embedding in high-dimensional Gaussian mixture block models.
Predict covariance from features using convex optimization.
This paper considers inference over distributed linear Gaussian models using factor graphs and Gaussian belief propagation (BP). The distributed inference algorithm involves only local computation of the information matrix and of the mean vector, and message passing between neighbors. Under broad conditions, it is show…
The standard state-of-the-art backend for text-independent speaker recognizers that use i-vectors or x-vectors, is Gaussian PLDA (G-PLDA), assisted by a Gaussianization step involving length normalization. G-PLDA can be trained with both generative or discriminative methods. It has long been known that heavy-tailed PLD…
We study the problem of estimating the mean of a random vector given a sample of independent, identically distributed points. We introduce a new estimator that achieves a purely sub-Gaussian performance under the only condition that the second moment of exists. The estimator is based on a novel concept of a…
In this paper, we propose an auto-encoder based generative neural network model whose encoder compresses the inputs into vectors in the tangent space of a special Lie group manifold: upper triangular positive definite affine transform matrices (UTDATs). UTDATs are representations of Gaussian distributions and can strai…
Calculates local Granger causality for Gaussian and nonlinear systems.
A new copula model for multi-attribute data using optimal transport.
Study analyzes perturbations in singular subspaces under random noise.
The paper proposes deep normalization to improve speaker recognition performance.
RVGP learns vector fields over unknown manifolds, preserving singularities.
Proposes using entity embedding vectors to improve Gaussian Process models for knowledge transfer across cell lines.
Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued rando…
Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of support vectors that defines a boundary around data. When dealing with online or large data, existing batch SVDD methods have to be rerun in eac…
The paper examines conditions for Lagrangian surfaces in Kähler-Einstein manifolds.
Improved flow matching using Gaussian processes for better sample quality.
We propose a probabilistic enhancement of standard kernel Support Vector Machines for binary classification, in order to address the case when, along with given data sets, a description of uncertainty (e.g., error bounds) may be available on each datum. In the present paper, we specifically consider Gaussian distributi…
This paper presents a novel approach to speaker subspace modelling based on Gaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model is based on the idea of shared factors as in the Probabilistic Linear Discriminant Analysis (PLDA). GRBM hidden layer is divided into speaker and channel factors, herein t…
In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Vi…
Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-train…
We extend the mixtures of Gaussians (MOG) model to the projected mixture of Gaussians (PMOG) model. In the PMOG model, we assume that q dimensional input data points z_i are projected by a q dimensional vector w into 1-D variables u_i. The projected variables u_i are assumed to follow a 1-D MOG model. In the PMOG model…
Paper offers robust recovery for 1-bit sensing with partial Gaussian circulant matrices.
We build a model using Gaussian processes to infer a spatio-temporal vector field from observed agent trajectories. Significant landmarks or influence points in agent surroundings are jointly derived through vector calculus operations that indicate presence of sources and sinks. We evaluate these influence points by us…
Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained sig…
We develop time-uniform confidence spheres for estimating means of random vectors.