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.
Enhances sensitivity analysis for correlated inputs.
A new method scales CCA parameters by input to learn more correlated representations.
Biological neurons learn tensor decompositions of higher-order correlations using nonlinear Hebbian plasticity.
A new method for Gaussian Processes handles mixed continuous and categorical inputs.
Automates machine learning of correlations between knot invariants.
RFMs transition from linear to nonlinear under specific input-label correlation.
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
Neural networks learn faster with correlated latent variables.
In this paper we use wavelet concepts to show that correlation coefficient between two financial data's is not constant but varies with scale from high correlation value to strongly anti-correlation value This studies is important because correlation coefficient is used to quantify degree of independence between two va…
We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression. Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training. Unlike axis-aligned alternatives…
New rationalization method avoids spurious correlations.
This paper benchmarks Bayesian models' ability to estimate predictive correlations, especially for active learning.
Memory capacity of DAM scales exponentially with feature separation, unaffected by correlations.
Sparse codes improve optimal control tasks with correlated inputs.
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 novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent comp…
Non-symmetric rectangular correlation matrices occur in many problems in economics. We test the method of extracting statistically meaningful correlations between input and output variables of large dimensionality and build a toy model for artificially included correlations in large random time series.The results are t…
Proposes -CCA for sparse CCA with improved representation learning.
Biological neural network mimics CCA for multi-channel data.
Global Navigation Satellite System (GNSS) signals are subject to different kinds of events causing significant errors in positioning. This work explores the application of Machine Learning (ML) methods of anomaly detection applied to GNSS receiver signals. More specifically, our study focuses on multipath contamination…
The paper tackles spurious correlations in machine learning models and introduces counterfactual invariance.
Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weigh…
Bayesian regularization tackles collinearity in large-scale systems with correlated inputs.
Canonical correlation analysis (CCA) is a technique to find statistical dependencies between a pair of multivariate data. However, its application to high dimensional data is limited due to the resulting time complexity. While the conventional CCA algorithm requires polynomial time, we have developed an algorithm that …
Financial correlations play a central role in financial theory and also in many practical applications. From theoretical point of view, the key interest is in a proper description of the structure and dynamics of correlations. From practical point of view, the emphasis is on the ability of the developed models to provi…
Diffusion models learn simple statistics before complex ones, revealing a sample complexity exponent.
This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.
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…
In this paper, we apply tools from the random matrix theory (RMT) to estimates of correlations across volatility of various assets in the S&P 500. The volatility inputs are estimated by modeling price fluctuations as GARCH(1,1) process. The corresponding correlation matrix is constructed. It is found that the distribut…
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…
In this study, the fluctuation-dissipation theory is invoked to shed light on input-output interindustrial relations at a macroscopic level by its application to IIP (indices of industrial production) data for Japan. Statistical noise arising from finiteness of the time series data is carefully removed by making use of…
Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our mai…
A new model predicts spatially varying inland flooding from time-varying inputs.
Gradient descent aligns neural feature matrices with pre-activation tangent features.
We present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random matrices, and gives an explicit expression for the interval where singular values are…
CVAE improves stock volume forecasting with advanced input variables.
Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning. The underlying premises are that there exist correlations among input parts and among output targets, and the …
Paper forecasts stock correlations using a hybrid model combining graph neural networks and transformers.
If the probability of default parameters (PDs) fed as input into a credit portfolio model are estimated as through-the-cycle (TTC) PDs stressed market conditions have little impact on the results of the capital calculations conducted with the model. At first glance, this is totally different if the PDs are estimated as…
Study neural networks by mapping correlations, revealing essential statistics.
GRU-PFG model extracts inter-stock correlations from stock factors using graph neural networks.
Data structure affects deep learning performance, study finds.
Proposes a model to detect changes in multivariate time series data.
A new framework evaluates model performance on single input points, revealing insights into data and model structure.
LMGPs extend GPs to handle mixed data, offering better accuracy and interpretability.
Symmetry of neural network densities can be determined from correlation functions.
SMM preserves matrix data structure for SVM classification.
Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated. Sparse CCA is NP-hard. We propose a novel combinatorial algorithm for s…