Study mutual info for community detection with covariate and correlated networks.
problem Community detection with covariate and correlated networks.
method Asymptotic upper bound and MMSE matrix heuristic analysis.
result Explicit characterization of combined information effects.
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
problem Efficiently clustering data with limited pairwise similarity information.
method Developed principled active learning approach using information-theoretic acquisition functions.
result Significantly outperforms existing baselines in clustering accuracy and query efficiency.
CopulaGNN integrates graph representational and correlational roles for better node-level predictions.
problem Graphs encode diverse roles in node-level prediction tasks, but GNNs struggle with correlational information.
method Copula theory to describe multivariate dependence, integrating representational and correlational graph information.
result CopulaGNN improves GNN performance on regression tasks by leveraging both types of graph information.
Study neural networks by mapping correlations, revealing essential statistics.
problem Understanding information processing in trained neural networks.
method Characterize neural network as distribution transformations, focusing on correlation functions.
result Higher-order correlations are crucial for internal layers, while input layer captures more.
Kernel methods linked to feature subspaces and maximal correlation kernels.
problem Understanding kernel methods and their relationship to feature extraction.
method Established a correspondence between feature subspaces and kernels, introduced maximal correlation kernels, and demonstrated their optimality.
result Kernel SVM on maximal correlation kernel achieves minimum prediction error.
High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…
We study graph matching with correlated Gaussian features and find thresholds for exact recovery.
problem Graph matching with correlated Gaussian features.
method Information-theoretic thresholds and conditions for exact and almost exact recovery.
result Contextual information introduces a richer structure, with thresholds for exact and almost exact recovery no longer coinciding.
The study uses DCC for financial market analysis, revealing hidden correlations.
problem Identifying hidden nonlinear correlations in financial markets.
method Agglomerative hierarchical clustering with distance correlation coefficient.
result DCC reveals more information than Pearson correlation for financial data.
This paper sets thresholds for recovering vertex correspondences in partially correlated graphs.
problem Recovering hidden vertex correspondences in partially correlated graphs.
method Proposed partially correlated Erdős-Rényi graphs model; information-theoretic thresholds; correlated functional digraphs.
result Optimal rates for partial and exact recovery of vertex correspondences.
A new algorithm PD improves stock-correlation network clustering and robustness.
problem Improving clustering and robustness of stock-correlation networks.
method Proposes a new proportional degree algorithm to filter information on a complete graph of normalised mutual information.
result The PD algorithm produces a network with better homogeneity and robustness compared to PMFG.
We show that the last few components in principal component analysis of the correlation matrix of a group of stocks may contain useful financial information by identifying highly correlated pairs or larger groups of stocks. The results of this type of analysis can easily be included in the information an investor uses …
New method disentangles latent subspaces under correlation shifts.
problem Correlations between factors of variation make disentanglement models less robust.
method Enforces independence between subspaces conditioned on available attributes using adversarial CMI minimization.
result Models are disentangled and robust under correlation shifts, including in weakly supervised settings.
Deep learning model estimates mutual information with low bias and variance.
problem Estimating mutual information between continuous variables.
method Supervised deep learning approach using Linfoot informational correlation as labels.
result Lower bias and variance compared to other methods.
We construct and analyze symmetrized delay correlation matrices for empirical data sets for atmopheric and financial data to derive information about correlation between different entities of the time series over time. The information about correlations is obtained by comparing the results for the eigenvalue distributi…
New tensor framework connects Fisher information, hypergraphs, and multi-observable correlations.
problem Missing structure in pairwise Fisher graphs for multi-observable radiation patterns.
method Higher-order Fisher tensors and natural exponential-family coordinates.
result Exact triality of Fisher tensors, cumulants, and hypergraphs.
Proposes NCMN to remove feature correlation in multiplicative noise.
problem High feature correlation in multiplicative noise reduces network performance.
method Exploits batch normalization to remove correlation effect.
result Significantly improves performance on image classification tasks.
New framework improves multivariate time series forecasting by minimizing redundant information.
problem Improving multivariate time series forecasting with deep learning techniques.
method Cross-variable Decorrelation Aware feature Modeling (CDAM) and Temporal correlation Aware Modeling (TAM) to refine Channel-mixing and exploit temporal correlations.
result Significantly surpasses existing models in comprehensive tests.
We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globaliz…
In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable. Such information is vital since it completely determines the direction in which two variables are associated. In this work, we resort to information theoretical approaches to achieve two fundamental goals: Fir…
The paper sets thresholds for testing correlation in hypergraphs, distinguishing between independent and correlated states.
problem Testing correlation between two hypergraphs under different models.
method Derives sharp information-theoretic thresholds for distinguishing between null and alternative hypotheses.
result The testing threshold decreases as the hypergraph's uniformity (m) increases, making correlation testing easier for higher uniformity.
A framework for navigating environments with spatially correlated obstacles and uncertain blockage status.
problem Navigation in environments with spatially correlated obstacles of uncertain blockage status.
method Modeling spatial correlation with Gaussian Random Field, developing Bayesian belief updates, proposing a two-stage learning framework with offline and online phases.
result Consistent performance gains over baselines in environments with adversarial interruptions or clustered natural hazards.
Proposes a flexible MGP model for dynamic, sparse correlations.
problem Handling dynamic and sparse correlations in multivariate data.
method Non-stationary MGP with dynamic spike-and-slab prior and EM algorithm.
result Captures dynamic and sparse correlations effectively.
We use Random Matrix Theory (RMT) and information theory to analyze the correlations and flow of information between 64,939 news from The New York Times and 40 world financial indices during 10 months along the period 2015-2016. The set of news was quantified and transformed into daily polarity time series using tools …
A new framework extracts informative features from multiple data types.
problem Finding maximally correlated feature representations from multimodal data.
method Soft-HGR framework, which relaxes whitening constraints while preserving feature geometry.
result Soft-HGR learns more informative feature mappings and is more efficient to optimize.
Study sharpens threshold for matching correlated graphs without labels.
problem Matching latent vertex correspondences in correlated random graphs.
method Analyzes information-theoretic limits for correct vertex matching in sub-sampled graphs.
result Establishes a sharp information-theoretic threshold for vertex matching recovery.
Paper studies vertex correspondence recovery in correlated graphs with node features.
problem Recovering hidden vertex correspondence between two correlated graphs with observed edge weights and node features.
method Introduced featured correlated Gaussian Wigner model and proposed QPAlign algorithm for quadratic programming relaxation.
result Characterized optimal information-theoretic thresholds for exact and partial recovery of latent mapping.
Reduces data leakage in distributed deep learning models.
problem Prevents reconstruction of sensitive raw data patterns during client communications.
method Reduces distance correlation between raw data and learned representations.
result Resilient to reconstruction attacks while maintaining model accuracy.
A new method for real-time CCA on streaming data.
problem Finding correlated features in online data streams.
method Sliding Window Informative Canonical Correlation Analysis (SWICCA) using streaming PCA.
result SWICCA provides real-time CCA components in high dimensions with theoretical guarantees.
Designs new functionals for ranking joint probability distributions based on correlations.
problem Ranking joint probability distributions based on their correlations.
method Using first principles from inference, a set of functionals are designed with the Principle of Constant Correlations (PCC) guiding the construction.
result The n-partite information (NPI) uniquely determines whether inferential transformations preserve, destroy, or create correlations. Enhances community detection in correlated networks with node attributes.
problem Community detection in multiple networks with correlated node attributes and edges.
method Introduced the correlated Contextual Stochastic Block Model (CSBM), developed a two-step matching procedure.
result Algorithm recovers exact node correspondence, enabling enhanced community detection.
Many predictive tasks, such as diagnosing a patient based on their medical chart, are ultimately defined by the decisions of human experts. Unfortunately, encoding experts' knowledge is often time consuming and expensive. We propose a simple way to use fuzzy and informal knowledge from experts to guide discovery of int…
In this paper, we study a continuous time structural asset value model for two correlated firms using a two-dimensional Brownian motion. We consider the situation of incomplete information, where the information set available to the market participants includes the default time of each firm and the periodic asset value…
We show that the Kullback-Leibler distance is a good measure of the statistical uncertainty of correlation matrices estimated by using a finite set of data. For correlation matrices of multivariate Gaussian variables we analytically determine the expected values of the Kullback-Leibler distance of a sample correlation …
We formulate learning of a binary autoencoder as a biconvex optimization problem which learns from the pairwise correlations between encoded and decoded bits. Among all possible algorithms that use this information, ours finds the autoencoder that reconstructs its inputs with worst-case optimal loss. The optimal decode…
A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.
problem Selecting models for ensemble learning to improve performance and reduce correlation issues.
method Formulate budgeted ensemble selection as maximizing mutual information, use Gaussian-copula to model correlated errors, propose a greedy mutual-information selection algorithm.
result Our method consistently outperforms strong baselines across multiple datasets.
Study on price-volume correlation fractal features and market type effects.
problem Understanding the fractal features and market type effects of price-volume correlation.
method Applied MF-DXA method to analyze price, trading volume, and their coupling.
result Price, trading volume, and price-volume coupling exhibit power law and multifractal properties.
Financial correlation matrices measure the unsystematic correlations between stocks. Such information is important for risk management. The correlation matrices are known to be ``noise dressed''. We develop a new and alternative method to estimate this noise. To this end, we simulate certain time series and random matr…
We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective. Intuitively, the optimization searches for a set of latent factors that best explain the correlations in the data as measured by multivariate mutual informatio…
CRCCA framework improves non-linear CCA with compressed representations.
problem Non-linear CCA for multi-view data with limited samples.
method Information-theoretic compressed representation framework (CRCCA) based on lattice quantization.
result The CRCCA framework provides theoretical bounds and optimality conditions, offering a flexible and computationally efficient solution.
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…
We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several other quantitative descriptions of asset price dynamics in financial markets. Specifically, we discuss how to define and obtain hierarchical …
A privacy-constrained information extraction problem is considered where for a pair of correlated discrete random variables (X,Y) governed by a given joint distribution, an agent observes Y and wants to convey to a potentially public user as much information about Y as possible without compromising the amount of …
Improved eigenvalue distribution method for financial data.
problem Noise and complexity in financial markets.
method Matrix H theory, hierarchical structure, informational cascade.
result Captures a larger fraction of data variance in financial markets.
Alpha-based performance evaluation may fail to capture correlated residuals due to model errors. This paper proposes using the Generalized Information Ratio (GIR) to measure performance under misspecified benchmarks. Motivated by the theoretical link between abnormal returns and residual covariance matrix, GIR is deriv…
This work improves wireless network learning by using side-information about interference.
problem Improving online learning algorithms in wireless networks.
method Exploiting side-information like interference levels to improve learning algorithms.
result Improved learning algorithms achieve higher throughput with fewer samples.
Maximal correlation framework improves fairness in machine learning algorithms.
problem Ensuring fairness in machine learning algorithms.
method Introducing maximal correlation framework for fairness constraints and deriving regularizers.
result The approach provides smooth performance-fairness tradeoff curves and competitive performance.
Paper presents a technique using Spearman's Rank Correlation Coefficient for KE in TDs.
problem Extracting common characteristics and grouping similar TDs.
method Spearman's Rank Correlation Coefficient (SRCC) for KE.
result SRCC proves a comprehensive measure for high-quality KE.
Bayesian method for dynamic correlation matrices improves accuracy and responsiveness.
problem Challenges in estimating time-varying correlation matrices, including slow adaptation, insufficient regularization, and diffuse uncertainty.
method Low-rank factor representation with dynamic shrinkage prior and multivariate factor stochastic volatility model.
result Improved accuracy and responsiveness compared to competing methods in various challenging scenarios.