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.
New sampling-based approach for filtering problems using multiplicative Gaussian functions.
Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a model that captures the latent information related to these conditions and generalize to a new one with few data? We present a new model ca…
Extends Gaussian Process regression for handling multiple prior distributions.
Paper adapts multiplicative weights method to Gaussian graphical models.
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables,…
Enhances robustness of MOGP regression for multiple correlated outputs.
A new method for Gaussian filtering using gradient flows and Wasserstein metrics.
Alzheimer's disease is a major cause of dementia. Its diagnosis requires accurate biomarkers that are sensitive to disease stages. In this respect, we regard probabilistic classification as a method of designing a probabilistic biomarker for disease staging. Probabilistic biomarkers naturally support the interpretation…
We consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices that best explains the data and by doing so improve the generalisation on unseen data. Sparsity in the kernel weights is obtained by adopting a hierarchical Bayesian approa…
FGN models networks with fractal structures using Gaussian Multiplicative Chaos.
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose log intensities are given by linear combinations of latent functions drawn from Gaussian process priors. The combination coefficients are als…
The paper explores using machine learning for yield curve calibration in multiple markets.
DeepICMGP surrogate models multiple outputs efficiently.
The paper introduces a method for learning nonparametric Volterra kernels using Gaussian processes.
Gaussian graphical model is a graphical representation of the dependence structure for a Gaussian random vector. It is recognized as a powerful tool in different applied fields such as bioinformatics, error-control codes, speech language, information retrieval and others. Gaussian graphical model selection is a statist…
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…
Designs an MLP from LDA for multi-Gaussian class classification.
This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism d…
The paper explores the identifiability and interpretability of Gaussian process models using different kernel structures.
Statistical-computational gap found in aligning multiple Gaussian graphs.
Bayesian ODEs with Gaussian processes infer unknown dynamics from data.
Method estimates multiple related Gaussian distributions using Laplacian regularization.
Study reveals structure of local minima in GMMs, identifying key cluster centers.
This dissertation advances scalable Gaussian processes using iterative methods and pathwise conditioning.
Model financial time series with MOGP for imputation and prediction.
The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades. Many clustering algorithms: such as k-means and spectral clustering are proposed based on the single sample assumption. However, in real life, …
The paper integrates multiple Gaussian process predictions using Monte Carlo sampling.
Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several co…
Paper speeds up Gaussian process inference using Matérn kernels.
G-GLN extends GLNs to multiple regression and density modeling.
We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in -dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accurac…
Proposes a new model for mixed membership in Gaussian mixture.
A new method reduces energy consumption in machine learning by using multiple, less costly data sources.
New methods discover causal relationships from multiple related data views.
Two new methods improve clustering with missing data.
Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.
Optimizes black-box functions with varying costs across multiple sources.
Multi-output Gaussian processes have received increasing attention during the last few years as a natural mechanism to extend the powerful flexibility of Gaussian processes to the setup of multiple output variables. The key point here is the ability to design kernel functions that allow exploiting the correlations betw…
Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modeling gene expression collected on multiple tissu…
The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning. In this paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas for its…
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compa…
The paper proposes a new method for modeling and quantifying uncertainty in multiple closed curves.
A linear non-Gaussian structural equation model called LiNGAM is an identifiable model for exploratory causal analysis. Previous methods estimate a causal ordering of variables and their connection strengths based on a single dataset. However, in many application domains, data are obtained under different conditions, t…
Deep random feature models are analyzed for their performance with exact asymptotic expressions.
We develop an automated variational method for inference in models with Gaussian process (GP) priors and general likelihoods. The method supports multiple outputs and multiple latent functions and does not require detailed knowledge of the conditional likelihood, only needing its evaluation as a black-box function. Usi…
Estimates target GGM using auxiliary studies with false discovery rate control.
AdaPT-GMM improves multiple testing power with covariates.
Study of Gaussian distributions using entropic Gromov-Wasserstein and inner product Gromov-Wasserstein.