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.
HTFM improves mode coverage and tail-statistic recovery for heavy-tailed data.
New theory allows ICA without assuming non-Gaussian sources.
A new method reduces energy consumption in machine learning by using multiple, less costly data sources.
Study uses Wasserstein distance to identify causal orders and unmix sources.
Optimizes black-box functions with varying costs across multiple sources.
Develops large-sample theory for non-stationary source separation.
PDGMM-VAE uses adaptive priors for better ICA recovery.
Paper tackles online multi-source domain adaptation using Gaussian mixtures and dictionary learning.
Proposes LVGP for multi-source data fusion in science and engineering.
Plug-and-play L-GM-AMP improves CS recovery for any i.i.d. source prior.
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…
New Gaussian min-max theorem extends classical results to non-i.i.d. Gaussian matrices.
AR-Flow VAE improves blind source separation with flexible autoregressive priors.
StrEBM learns distinct latent components for better source separation.
Proposes a framework to fuse heterogeneous data sources for better modeling.
Enhanced FastMNMF for better speech separation.
In this paper we address a classification problem where two sources of labels with different levels of fidelity are available. Our approach is to combine data from both sources by applying a co-kriging schema on latent functions, which allows the model to account item-dependent labeling discrepancy. We provide an exten…
Gaussian process (GP) audio source separation is a time-domain approach that circumvents the inherent phase approximation issue of spectrogram based methods. Furthermore, through its kernel, GPs elegantly incorporate prior knowledge about the sources into the separation model. Despite these compelling advantages, the c…
We propose computationally efficient encoders and decoders for lossy compression using a Sparse Regression Code. The codebook is defined by a design matrix and codewords are structured linear combinations of columns of this matrix. The proposed encoding algorithm sequentially chooses columns of the design matrix to suc…
StrADiff separates sources from mixtures without labels, using structured priors.
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic tr…
LMGPs enable efficient, accurate data fusion across multiple data sources.
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming languages. In this …
CCVFM uses coreset to improve generative models by refining residual flows.
Paper tackles MSDA with GMMs and OT, improving over prior art.
Method quantifies sensitivity of reliability analysis to uncertainty sources.
SYNC generates synthetic data from aggregated sources using Gaussian copulas.
In magnetoencephalography (MEG) the conventional approach to source reconstruction is to solve the underdetermined inverse problem independently over time and space. Here we present how the conventional approach can be extended by regularizing the solution in space and time by a Gaussian process (Gaussian random field)…
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian independent sources. The problem can be seen as a likelihood maximization problem. We i…
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals. Conventional neural separation methods require a lot of supervised data to achieve excellent performance. Although multichannel methods based on spatial information can work without such training …
Study derives error decay rates for kernel classification under source and capacity conditions.
In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration. The parallelization scheme arises naturally from the modular computational structure w.r.t. datapoints in the sparse Gaussian process formulation. Additionally, the computational bottleneck is …
We study a new class of codes for lossy compression with the squared-error distortion criterion, designed using the statistical framework of high-dimensional linear regression. Codewords are linear combinations of subsets of columns of a design matrix. Called a Sparse Superposition or Sparse Regression codebook, this s…
A novel Gaussian process approach for deconvolution of missing data signals.
A new method for binary ICA using non-stationary sources.
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…
This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix. This issue is formulated in a…
This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial cha…
FlowMO uses Gaussian Processes for molecular property prediction with uncertainty.
New algorithm identifies causal effects in latent confounding models.
Proposes a method to combine datasets with missing values using Gaussian process latent variables.
We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions. Complex symmetric alpha-stable distributions have been recently showed to better model audio signals in the time-frequency domain than classical Gaussian distributions thanks to their larger dynamic range. However, infer…
Gaussian Processes improve data interpolation from diverse experiments.
In this tutorial we explain the inference procedures developed for the sparse Gaussian process (GP) regression and Gaussian process latent variable model (GPLVM). Due to page limit the derivation given in Titsias (2009) and Titsias & Lawrence (2010) is brief, hence getting a full picture of it requires collecting resul…
Algorithm finds frequencies, amplitudes, and phases of sinusoids in noisy data.
Meta-models predict model hyperparameters for NDT experiments.
Survey of Gaussian process constraints for modeling expensive data.
We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic…