Proposes a hierarchical clustering method for positive and negative dissimilarities.
problem Clustering dissimilarities, especially positive and negative.
method Hierarchical correlation clustering followed by tree preserving embedding.
result Performance on various datasets.
Paper proves global convergence of NCELM model.
problem Ensuring global convergence of NCELM model.
method Two-stage process: random base learners and NCL penalty term updates.
result Global convergence of NCELM proved using Banach theorem.
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.
The paper explores the relationship between joint mixability and negative dependence structures.
problem Understanding the connection between joint mixability and various negative dependence concepts.
method Analyzes the properties of joint mixes and their relation to negative dependence structures.
result Derives necessary and sufficient conditions for a joint mix to be negatively dependent.
Non-negative matrix factorization is a basic tool for decomposing data into the feature and weight matrices under non-negativity constraints, and in practice is often solved in the alternating minimization framework. However, it is unclear whether such algorithms can recover the ground-truth feature matrix when the wei…
This study uses local Gaussian correlation to analyze stock return tails, revealing more sensitive network properties.
problem Misleading results from Pearson correlation in financial networks.
method Local Gaussian correlation coefficient for capturing nonlinear dependence and heavy-tailed distributions.
result Local Gaussian correlation network among negative tails is more sensitive to stock market risks.
Paper develops active learning for clustering unknown pairwise similarities.
problem Learning positive and negative pairwise similarities efficiently.
method Generic active learning framework for correlation clustering.
result Demonstrates effectiveness of query strategies in clustering.
A new framework evaluates model performance on single input points, revealing insights into data and model structure.
problem Traditional evaluation methods in machine learning are insufficient for understanding model performance and data structure.
method Developed a pointwise framework to measure model performance on individual input points, analyzing the relationship between pointwise and average performance.
result Profiles of data points reveal different types of correlations between pointwise and average performance, challenging existing models of learning.
Antithetic noise improves diffusion models' uncertainty quantification.
problem Improving uncertainty quantification in diffusion models.
method Pairing each noise sample with its negation, leading to strong negative correlation.
result Substantially more reliable uncertainty quantification with up to 90% narrower confidence intervals.
GNCL algorithm controls diversity in deep ensembles.
problem Managing bias and variance in deep ensembles.
method Generalized bias-variance decomposition for arbitrary loss functions, leading to GNCL algorithm.
result Explicit control over ensemble diversity and smooth interpolation between independent and joint training.
Study analyzes factors affecting capital adequacy in Bangladesh's banks.
problem Factors influencing capital adequacy in commercial banks in Bangladesh.
method Fixed Effect, Random Effect, and Pooled Ordinary Least Square (POLS) methods.
result Several independent variables significantly affect capital adequacy, with specific relationships between leverage, liquidity risk, and other factors.
Stock correlations is crucial to asset pricing, investor decision-making, and financial risk regulations. However, microscopic explanation based on agent-based modeling is still lacking. We here propose a model derived from minority game for modeling stock correlations, in which an agent's expected return for one stock…
ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.
problem Impact of Bitcoin ETF approval on Bitcoin's relationships with traditional assets.
method Rolling correlation analysis, Chow tests, and DCC-GARCH models.
result Bitcoin's correlation with equities increased significantly post-ETF approval, while its relationship with gold stabilized and remained negatively correlated with fiat currencies.
Factorial moments are convenient tools in particle physics to characterize the multiplicity distributions when phase-space resolution (Δ) becomes small. They include all correlations within the system of particles and represent integral characteristics of any correlation between these particles. In this letter, we sh…
New insights into negative sampling for graph representation learning.
problem Challenges in generating high-quality graph representations for large node sets.
method Theoretical analysis and derivation of negative sampling distribution correlation, proposing MCNS method.
result The negative sampling distribution should be positively but sub-linearly correlated to the positive sampling distribution.
Estimates vaccine effectiveness and immune correlates in TND studies with missing data.
problem Confounding and missing data in TND studies of vaccine effectiveness and immune correlates.
method Targeted maximum likelihood estimation using a semiparametric logistic regression model.
result Valid causal inference of vaccine effectiveness and immune correlates in TND studies with missing exposure data.
New methods improve cross-correlation analysis of time series data.
problem Controversies in Multifractal detrended cross-correlation analysis.
method Proposes new options to handle negative cross-covariance.
result Improved robustness in multifractal spectrum analysis.
The study finds significant power-law cross correlations in Bitcoin's return-volatility dynamics.
problem Investigating asymmetry in Bitcoin's return-volatility relationships.
method Analysis of daily and high-frequency Bitcoin data to identify cross correlations.
result Power-law cross correlations between returns and future volatilities are observed, indicating long-range dependencies.
Paper tackles target shift in zero-shot learning using adversarial learning.
problem Target shift in zero-shot learning leads to performance degradation.
method Estimates target shift using class-attribute mapping and applies grouped adversarial learning.
result Improves zero-shot learning performance on multiple datasets.
We study the relation between serial correlation of financial returns and volatility at intraday level for the S&P500 stock index. At daily and weekly level, serial correlation and volatility are known to be negatively correlated (LeBaron effect). While confirming that the LeBaron effect holds also at intraday level, w…
Logit correction improves model performance by correcting spurious correlations.
problem Spurious correlations lead to poor model performance during inference.
method Proposes logit correction (LC) loss to mitigate spurious correlations.
result LC loss outperforms state-of-the-art solutions by 5.5% absolute improvement.
CMLE reduces spurious correlations in deep models.
problem Spurious correlations in deep learning models.
method Counterfactual Maximum Likelihood Estimation (CMLE) on interventional distribution.
result CMLE outperforms regular MLE in out-of-domain generalization and spurious correlation reduction.
We study the relationship between catastrophic forgetting and properties of task sequences. In particular, given a sequence of tasks, we would like to understand which properties of this sequence influence the error rates of continual learning algorithms trained on the sequence. To this end, we propose a new procedure …
While on some natural distributions, neural-networks are trained efficiently using gradient-based algorithms, it is known that learning them is computationally hard in the worst-case. To separate hard from easy to learn distributions, we observe the property of local correlation: correlation between local patterns of t…
Modeling correlated mutations in cancer for personalized treatment.
problem Identifying mutations for personalized cancer therapy in heterogeneous profiles.
method Proposed correlated zero-inflated negative binomial process with mixed beta-Bernoulli and variational inference.
result Identified biologically relevant correlations between somatic mutations.
LMMVAE improves VAE for correlated data by separating latent variables into fixed and random parts.
problem Correlated data in tabular and image datasets.
method Integrates random effects into VAE architecture, separating latent variables into fixed and random parts.
result Significant improvement in reconstruction error and likelihood loss on unseen data.
This paper introduces a novel deep learning based method, named bridge neural network (BNN) to dig the potential relationship between two given data sources task by task. The proposed approach employs two convolutional neural networks that project the two data sources into a feature space to learn the desired common re…
Meta-learning shows negative transfer between tasks, which MetaCRL addresses.
problem Negative transfer between tasks in meta-learning.
method Structural Causal Models (SCMs) and MetaCRL to eliminate task confounders.
result MetaCRL achieves state-of-the-art performance in various benchmark datasets.
Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transitive relational data (e.g. entailment graphs). However, OE learns a deterministic knowledge base, limiting expressiveness of queries and the…
New model explains price dynamics of Bitcoin with psychological factors.
problem Understanding price variations in cryptocurrency markets with psychological factors.
method Extended agent-based model with heterogeneous psychological parameters.
result Model shows diverse dynamics based on psychological correlation.
Biclustering is an unsupervised data mining technique that aims to unveil patterns (biclusters) from gene expression data matrices. In the framework of this thesis, we propose new biclustering algorithms for microarray data. The latter is done using data mining techniques. The objective is to identify positively and ne…
We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in …
The study identifies spurious correlations in high-dimensional regression and quantifies their impact.
problem Spurious correlations in high-dimensional regression models.
method Statistical characterization of spurious correlations, quantifying their amount via ridge regularization.
result The value of regularization strength that minimizes test loss is in an interval where spurious correlations increase.
Statistical evaluations of the economic mobility of a society are more difficult than measurements of the income distribution, because they require to follow the evolution of the individuals' income for at least one or two generations. In micro-to-macro theoretical models of economic exchanges based on kinetic equation…
We study the impact of volatility on intraday serial correlation, at time scales of less than 20 minutes, exploiting a data set with all transaction on SPX500 futures from 1993 to 2001. We show that, while realized volatility and intraday serial correlation are linked, this relation is driven by unexpected volatility o…
We study power-law correlations properties of the Google search queries for Dow Jones Industrial Average (DJIA) component stocks. Examining the daily data of the searched terms with a combination of the rescaled range and rescaled variance tests together with the detrended fluctuation analysis, we show that the searche…
With the random matrix theory, we study the spatial structure of the Chinese stock market, American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive …
The study shows interest rates impact investment and funding negatively but positively on dividend decisions.
problem The effect of interest rates on financial decisions like investment, funding, and dividend.
method Correlation coefficient analysis and descriptive methods.
result Interest rates have a negatively insignificant effect on investment and funding decisions, but positively moderate effect on dividend decisions.
The data scarcity of user preferences and the cold-start problem often appear in real-world applications and limit the recommendation accuracy of collaborative filtering strategies. Leveraging the selections of social friends and foes can efficiently face both problems. In this study, we propose a strategy that perform…
TimeCNN improves forecasting by refining cross-variable interactions over time.
problem Multivariate time series forecasting struggles with dynamic and multifaceted cross-variable correlations.
method TimeCNN uses timepoint-independent convolution kernels to capture evolving relationships among variables.
result TimeCNN outperforms state-of-the-art models in real-world datasets with significant computational and speed advantages.
The paper tackles fair correlation clustering with fairness constraints.
problem Minimizing disagreements while adhering to fairness constraints for clustering.
method Two variants of fairness constraints are considered: equal distribution and relative bounds. Approximation algorithms are developed for these constraints.
result Approximation algorithms for fair correlation clustering with theoretical guarantees and empirical validation.
ChatGPT predicts stock market movements based on Bloomberg headlines, showing a positive correlation over short to medium terms.
problem Predicting stock market movements using news headlines.
method Used a two-stage prompt approach with a dataset of Bloomberg market summaries from 2010 to 2023.
result ChatGPT's sentiment scores correlate positively with future equity market returns over short to medium terms, with a negative correlation over longer horizons.
Study shows economic policy uncertainty increases stock market crash risk during pandemic.
problem Impact of economic policy uncertainty on stock market crashes during the pandemic.
method Used GARCH-S model to estimate daily skewness as a proxy for crash risk, analyzed data from US stock market.
result Significantly negative correlation between economic policy uncertainty and stock market crash risk, stronger during pandemic.
A possible data source for the estimation of asset correlations is default time series. This study investigates the systematic error that is made if the exposure pool underlying a default time series is assumed to be homogeneous when in reality it is not. We find that the asset correlation will always be underestimated…
Graph Canonical Correlation Analysis improves CCA for multiomics datasets.
problem Limited ability of conventional CCA methods to incorporate structured patterns in cross-correlation matrices.
method Graph Canonical Correlation Analysis (gCCA) calculates canonical correlations based on the graph structure of cross-correlation matrices.
result gCCA outperforms competing CCA methods in simulations and multiomics dataset analysis.
Proposes integrating random effects into deep neural networks for better predictive performance.
problem Correlated data in real-life applications are not handled well by traditional deep neural networks.
method Uses mixed models with random effects to handle correlations in deep neural networks, minimizing Gaussian negative log-likelihood with SGD.
result Improves predictive performance over natural competitors in various correlation scenarios.
We use the P&L on a particular class of swaps, representing variance and higher moments for log returns, as estimators in our empirical study on the S&P500 that investigates the factors determining variance and higher-moment risk premia. This class is the discretisation invariant sub-class of swaps with Neuberger's agg…
Proposes TFCL to mitigate negative transfer in MTL by collaborating across features and tasks.
problem Negative transfer in Multi-Task Learning (MTL) due to dissimilar tasks.
method Task-Feature Collaborative Learning (TFCL) with heterogeneous block-diagonal structure regularizer and optimization method.
result Global convergence and block-diagonal structure recovery guarantees.