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.
Paper extends matrix-based Renyi's α-order entropy to multivariate data.
Data transformation, e.g. feature transformation and selection, is an integral part of any machine learning procedure. In this paper we introduce an information-theoretic model and tools to assess the quality of data transformations in machine learning tasks. In an unsupervised fashion, we analyze the transfer of infor…
Density destructors simplify complex PDFs to maximize entropy, linking to information theory.
Williams and Beer (2010) proposed a nonnegative mutual information decomposition, based on the construction of redundancy lattices, which allows separating the information that a set of variables contains about a target variable into nonnegative components interpretable as the unique information of some variables not p…
New estimator for joint entropy outperforms existing methods in various distributions.
A new method uses maximum entropy for time series analysis.
New GoF test improves change point detection in multivariate time series.
Inference for normal and Monte Carlo distributions using minimum relative entropy.
In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature rele…
Proposes a new method to better understand complex system interactions.
Proposes a method to compute information theory measures via Gaussianization.
We study a new ensemble of random correlation matrices related to multivariate Student (or more generally elliptic) random variables. We establish the exact density of states of empirical correlation matrices that generalizes the Marcenko-Pastur result. The comparison between the theoretical density of states in the St…
Paper extends multivariate rank tests for robust subspace detection.
While it is an important problem to identify the existence of causal associations between two components of a multivariate time series, a topic addressed in Runge et al. (2012), it is even more important to assess the strength of their association in a meaningful way. In the present article we focus on the problem of d…
Behavior of the entropy numbers of classes of multivariate functions with mixed smoothness is studied here. This problem has a long history and some fundamental problems in the area are still open. The main goal of this paper is to develop a new method of proving the upper bounds for the entropy numbers. This method is…
The paper develops approximations for Pearson's chi-square statistic and applies them to confidence intervals.
Software estimates inequality in random systems with changing communities.
Proposes PEID for analyzing synergistic causation in complex systems.
The space of probability densities is an infinite-dimensional Riemannian manifold, with Riemannian metrics in two flavors: Wasserstein and Fisher--Rao. The former is pivotal in optimal mass transport (OMT), whereas the latter occurs in information geometry---the differential geometric approach to statistics. The Rieman…
The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method (Idep) has recently been proposed for computing a two-predictor PID over discrete spaces. [arXiv:1709.06653] A lattice of maximum en…
Improved GoF statistics using entropy-regularized optimal transport for multivariate rank.
Estimating entropy and mutual information consistently is important for many machine learning applications. The Kozachenko-Leonenko (KL) estimator (Kozachenko & Leonenko, 1987) is a widely used nonparametric estimator for the entropy of multivariate continuous random variables, as well as the basis of the mutual inform…
New method efficiently interpolates nonparametric density estimators.
Accurately determining dependency structure is critical to discovering a system's causal organization. We recently showed that the transfer entropy fails in a key aspect of this---measuring information flow---due to its conflation of dyadic and polyadic relationships. We extend this observation to demonstrate that this…
We propose a test of independence of two multivariate random vectors, given a sample from the underlying population. Our approach, which we call MINT, is based on the estimation of mutual information, whose decomposition into joint and marginal entropies facilitates the use of recently-developed efficient entropy estim…
The paper applies Gaussianization to analyze Earth data, simplifying complex multivariate distributions.
In the world of modern financial theory, portfolio construction has traditionally operated under at least one of two central assumptions: the constraints are derived from a utility function and/or the multivariate probability distribution of the underlying asset returns is fully known. In practice, both the performance…
MPPN network improves long-term time series forecasting accuracy.
New method finds better loss functions for neural nets.
A new method is proposed to compute connectivity measures on multivariate time series with gaps. Rather than removing or filling the gaps, the rows of the joint data matrix containing empty entries are removed and the calculations are done on the remainder matrix. The method, called measure adapted gap removal (MAGR), …
The resolution and calibration of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for sin…
Suggests stopping criteria for feature selection using mutual information.
The matrix-based Renyi's α-entropy functional and its multivariate extension were recently developed in terms of the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel Hilbert space (RKHS). However, the utility and possible applications of these new estimators are rather new an…
The paper proposes a new method to assess financial risk and total risk in EU using Markov chains and copulas.
We discuss the connection between information and copula theories by showing that a copula can be employed to decompose the information content of a multivariate distribution into marginal and dependence components, with the latter quantified by the mutual information. We define the information excess as a measure of d…
Paper calculates KL divergence for isotropic Gaussian-Markov fields.
Effective nonparametric anomaly detection for high-dimensional data.
The paper examines stochastic ordering of Gini indexes for multivariate elliptical risks.
Gradient learning optimises MCMC proposal distributions.
PCA simplifies multivariate extreme data analysis.
Enhances RL by controlling policy stochasticity through trajectory entropy constraints.
hyppo simplifies multivariate hypothesis testing in Python.
This paper uses multivariate probability models to assess financial system risks.
The paper calculates bounds for risk metrics and entropies under partial information constraints.
The paper analyzes worst-case distortion risk metrics and weighted entropy under partial information.
RED CoMETS improves multivariate time series classification accuracy.
We study various specializations of the colored HOMFLY-PT polynomial. These specializations are used to show that the multivariable link invariants arising from a complex family of sl(m|n) super-modules previously defined by the authors contains both the multivariable Alexander polynomial and Kashaev's invariants. We c…
Regularized MFPCA smooths multivariate functional data for clearer patterns.