Rhino learns causal relationships from time series data with history-dependent noise.
problem Discovering causal relationships from time series data with non-linear relations, instantaneous effects, and history-dependent noise.
method Combines vector auto-regression, deep learning, and variational inference.
result Demonstrates better causal relationship discovery performance compared to baselines.
CDDN tackles visual relationship detection with context-dependent diffusion networks.
problem Combustion of combinatorial explosion in relation triplets detection.
method CDDN framework using semantic and visual scene graphs for adaptive information aggregation.
result CDDN achieves state-of-the-art performance on visual relationship detection datasets.
A new method makes adversarial domain adaptation aware of class relationships.
problem Ignoring inter-class semantic relationships in domain adaptation.
method RADA algorithm that aligns inter-class dependencies learned from domain discriminator with those from label predictor.
result Improves performance on benchmark datasets by incorporating class relationships.
New method learns relationships between independent data.
problem Learning relationships between independent data sets.
method A mix of quantile matching and deconvolution, requiring monotone dependency.
result Can be combined with matching to leverage both methods.
Analysis shows diversification of risks isn't always better.
problem The effectiveness of diversification in risk management.
method Examined individual risk characteristics and dependence relationships.
result Diversification is not always superior to non-diversification.
Proposes a new dependency function for measuring non-linear relationships.
problem Need for a general-purpose measure of dependency between random variables.
method Revision of ideal properties and proposal of a new dependency function.
result Proposes a new dependency function that meets all desired properties.
Algorithm detects lead-lag relationships in multivariate time series.
problem Understanding temporal dependencies between time series.
method Cluster-driven methodology based on dynamic time warping.
result Robust detection of lead-lag relationships in lagged multi-factor models.
Measuring dependence between two random variables is very important, and critical in many applied areas such as variable selection, brain network analysis. However, we do not know what kind of functional relationship is between two covariates, which requires the dependence measure to be equitable. That is, it gives sim…
Study examines lead-lag relationships between VIX and VIX futures markets over time.
problem Understanding dynamic interaction patterns between VIX and VIX futures markets.
method Utilized the symmetric thermal optimal path (TOPS) method to analyze time-dependent lead-lag relationships.
result Observed alternate lead-lag relationship instead of dominance between VIX and VIX futures markets.
New measure assesses predictive dependence between continuous variables, capturing non-functional relationships.
problem Quantifying the joint dependence between continuous random variables.
method Introduces a novel, fully non-parametric measure bounded [0,1] that assesses predictive accuracy loss.
result The measure captures a wide range of relationships, including non-functional ones, and is interpretable.
Causal relationships in time series with latent variables are discovered using LPCMCI.
problem Discovering causal relationships in complex, time-series data with hidden variables.
method Evaluated LPCMCI algorithm for finding generators compatible with multi-dimensional, autocorrelated time series with latent variables.
result LPCMCI performs better than random guessing but is not optimal.
A new method detects relationships between disparate data properties efficiently.
problem Detecting relationships between different data properties, especially in large datasets.
method Multiscale Graph Correlation (MGC) combines k-nearest neighbors, kernel methods, and multiscale analysis.
result MGC achieves statistical power comparable to existing methods with significantly fewer samples and provides insight into the relationship.
Discover novel multivariate relationships in time series data.
problem Capturing novel relationships between time series in complex systems.
method Introducing multipoles as linear relationships among more than two time series, identifying them as cliques of negative correlations in a correlation network.
result Almost all multipoles can be efficiently found using a clique-enumeration approach.
There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a d…
dGAP learns feature dependencies and predicts targets simultaneously.
problem Learning task-agnostic statistical dependencies and missing explicit feature dependencies.
method Jointly optimizes a neural dependency graph and target prediction loss.
result dGAP can recover correct feature dependencies and improve prediction accuracy.
Survey of three geometric frameworks for action-dependent field theories.
problem Understanding action-dependent field theories through geometric structures.
method Introduction and analysis of three geometric frameworks: k-contact, k-cocontact, and multicontact.
result Analysis of relationships among these geometric structures and comparison with other definitions.
A new method treats all variables equally in fitting data.
problem Fitting relationships to data with multiple variables, especially when dependent and independent variables are not clearly defined.
method A general method treating all variables impartially, using geometric mean functional relationships and correlation.
result The method provides coefficients that are easily calculated from covariances or correlations, making it scale-invariant and applicable to various units.
Investigates relationships between concordance measures and non-exchangeability in copulas.
problem Understanding the relationship between concordance measures and non-exchangeability in copulas.
method Examines five concordance measures (Spearman's rho, Kendall's tau, Gini's gamma, Blomqvist's beta, and footrule) and their connection to non-exchangeability in copulas.
result New method proposed for exploring the relationship between copula properties and measures of dependence.
Empirical study of leading measures of dependence for data analysis.
problem Identifying promising pairwise associations in data analysis.
method Extensive empirical evaluation of equitability, power against independence, and runtime of several measures of dependence.
result MICe is most equitable on functional relationships, while TICe is state-of-the-art in power against independence.
Study reveals lead-lag patterns between onshore and offshore RMB exchange rates.
problem Understanding the lead-lag relationship between onshore and offshore RMB exchange rates.
method Employed the thermal optimal path method to analyze daily and minute-scale data.
result Lead-lag patterns are influenced by market factors and US dollar appreciation.
The relationship between the size and the variance of firm growth rates is known to follow an approximate power-law behavior σ(S)∼S−β(S) where S is the firm size and β(S)≈0.2 is an exponent weakly dependent on S. Here we show how a model of proportional growth which treats firms as classes compos…
A new measure equitability helps identify significant relationships in high-dimensional data.
problem Identifying significant relationships in high-dimensional datasets with many weak relationships.
method Formalizes equitability as a property of measures of dependence, introduces interpretable intervals, and uses hypothesis testing equivalence.
result Equitability allows for well-powered tests distinguishing between trivial and non-trivial relationships and different strengths.
Flexible framework for transfer learning with optimal rates.
problem Inference about a target population using related source data.
method Adaptive transfer learning framework allowing covariate-dependent relationships.
result Achieves minimax optimal rates of convergence by adapting to transfer relationship.
New adaptive tests improve statistical dependence detection.
problem Testing statistical dependence between multivariate variables.
method Adaptive nonlinear monotonic transformations of distances.
result Empirical tests outperform existing methods.
Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both of those analyses are concentrated only on Pearson's correlation coefficient and thus intraday lead-lag relationships associated wi…
Proposes a new VAE model to avoid posterior collapse by modeling latent variable dependencies.
problem Posterior collapse in variational autoencoders due to assumption of factorized variational posterior.
method Introduces Gaussian Copula Variational Autoencoder (GCVAE) to model latent variable dependencies explicitly.
result Empirical results show GCVAE can avoid posterior collapse while maintaining competitive performance.
ReGENN improves time series forecasting by considering inter and intra-temporal relationships.
problem Achieving reliable predictions in real-world time series applications.
method ReGENN combines graph evolution with deep recurrent learning to model dynamic dependencies among multiple variables.
result Sound improvement of up to 64.87% over competing algorithms in time-series forecasting.
Study finds nonlinear relationship between stock correlation and multiscaling indicator.
problem Understanding the relationship between stock correlation and multiscaling in financial markets.
method Investigated the relationship between an indicator of multiscaling and stock correlation, considering capitalization and kurtosis.
result Observed a robust stylized fact of nonlinear dependence between multiscaling indicator and stock correlation across different markets.
Estimates temporal relationships in time-series data with latent variables.
problem Learning causal relationships between time series with latent memory.
method Developed an estimator for latent Markov processes with variable lags.
result Parameters can be learned consistently under genericity assumption.
Information theory provides ideas for conceptualising information and measuring relationships between objects. It has found wide application in the sciences, but economics and finance have made surprisingly little use of it. We show that time series data can usefully be studied as information -- by noting the relations…
New method finds dependent subspaces of multiple views for better data retrieval.
problem Finding relationships between multiple data views for analysis and prediction.
method Optimizes mappings for each view to maximize cross-view similarity between neighborhoods of data samples.
result The method outperforms alternatives in preserving cross-view neighborhood similarities and detecting local dependencies.
New margin measure improves deep learning generalization and robustness.
problem Unclear relationship between output margin and generalization for deep models.
method Introduced 'all-layer margin' for deep neural networks.
result Tighter generalization bounds for neural nets with no exponential depth dependency.
Python toolbox uncovers causal relationships from data.
problem Discovering causal relationships from observational data.
method End-to-end approach using algorithms from 'Bnlearn' and 'Pcalg', including pairwise causal discovery.
result Recovery of direct dependencies and causal relationships.
Contextualized ML learns context-dependent effects using deep learning.
problem Learning heterogeneous and context-dependent effects in data.
method Applying deep learning to the meta-relationship between contextual information and context-specific parametric models.
result Unified framework for cluster analysis and cohort modeling.
Introduces a new model for directed relationships in Gaussian data.
problem Learning directed relationships in Gaussian data.
method Developed a new directed graphical model (GGIM) from Gaussian data, leveraging stationary Gaussian processes on graphs.
result GGIMs can be framed as a LASSO problem and have a bound on the difference from the l1-norm penalized maximum log-likelihood estimate. Paper introduces MIC*, a new measure of dependence that is equitable and powerful.
problem Finding the strongest relationships in high-dimensional data sets.
method Introduces MIC*, a population measure of dependence, and defines three ways to view it. Proposes efficient algorithms for computing MIC* and a consistent estimator MICe.
result MICe and TICe show better equitability and power against independence.
Survey of methods to recover CI graphs from feature relationships.
problem Recovering conditional independence graphs from feature relationships.
method Traditional optimization methods and deep learning architectures are discussed.
result Advances in techniques to recover CI graphs are studied.
Deep learning method for semiparametric regression of spatial data.
problem Estimating relationships between response and covariates in spatially dependent data.
method A sparsely connected deep neural network with ReLU activation function.
result The method is consistent and can handle large datasets.
Econophysics explores power-law correlations in financial markets.
problem Analyzing long-range dependencies and power-law correlations in financial data.
method Generalization of methods from outside finance to financial time series, focusing on bivariate settings.
result Rapid development in econophysics has revealed new challenges and issues.
The paper develops a spectral theory for hypergraphs with edge-dependent vertex weights using random walks.
problem Lack of spectral theory for hypergraphs with edge-dependent vertex weights.
method Random walks on hypergraphs with edge-dependent vertex weights, deriving a random walk-based hypergraph Laplacian.
result Random walks on hypergraphs with edge-dependent vertex weights can capture higher-order relationships in data.
Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.
problem Understanding the relationship between teachers' advising relationships and their perceptions of satisfaction and students.
method Proposed a novel joint model of network and item responses (JNIRM) with correlated latent variables.
result Teachers' advising relationships contribute more to satisfaction and students than to influence over educational policies.
Local learning method selects covariates for causal effect estimation in the presence of latent variables.
problem Estimating causal effects from nonexperimental data with latent variables.
method Local learning approach that identifies valid adjustment sets for causal relationships.
result Ensures soundness and completeness of causal effect estimation under standard assumptions.
Model predicts operational risk using HMMs with economic covariates.
problem Predicting operational risk losses with time-dependent structures and economic covariates.
method Hidden Markov Models extended to multivariate observations with an auxiliary economic variable.
result Calibration results show relevance of including economic covariates.
Develops a new method to discover causal relationships from nonstationary time series data.
problem Challenges in inferring causal relationships from observational data, especially for nonstationary time series.
method State-Dependent Causal Inference (SDCI) for conditionally stationary time series.
result SDCI can recover underlying causal dependencies with provable identifiability for state-dependent causal structures.
Survey on learning with graph-dependent data, deriving new generalization bounds.
problem Traditional i.i.d. data assumption fails in many real-life applications.
method Collect and analyze graph-dependent concentration bounds, derive generalization bounds.
result New generalization bounds for graph-dependent data.
We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual…
Method detects lead-lag relationships in multivariate time series.
problem Discovering lead-lag relationships in multivariate time series.
method Clustering-driven methodology using sliding window and various clustering techniques.
result Robust lead-lag estimates across clusters enhance consistent relationships identification.
Unified framework for generating data by modeling causal and correlational dependencies.
problem Modeling both causal and correlational dependencies among latent factors.
method Causal-Correlation Variational Autoencoder (C2VAE) framework.
result Improves generation quality, disentanglement, and intervention fidelity.