On-device research index

arXiv research

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.

168,742 papers · 148 categories

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8 results for “block-dependence”

Functional Bias and Tangent-Space Geometry in Variational Inferencemath.ST

Geometric framework analyzes bias in variational inference for posterior functionals.

problem Analyzing the bias of posterior functionals under variational approximations.

method Developed a geometric framework to evaluate the bias of posterior functionals using the variational tangent space.

result The leading-order bias of a posterior functional is determined by its component orthogonal to the variational tangent space.

variational inference posterior functionals tangent-space geometry bias analysis mean-field approximation

2026-03-09abs ↗pdf ↗

Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problemsstat.ML

Core-Halo solves large-scale fixed-point problems by decentralizing updates.

problem Large-scale fixed-point equations with block dependencies.

method Core-Halo decomposition separates write ownership from read-only context, aligning with block-dependence structure.

result Core-Halo achieves near-centralized performance while retaining parallelism.

fixed-point decentralization multi-agent Bellman closure block-dependence

2026-05-09abs ↗pdf ↗

In the present paper we study a sparse stochastic network enabled with a block structure. The popular Stochastic Block Model (SBM) and the Degree Corrected Block Model (DCBM) address sparsity by placing an upper bound on the maximum probability of connections between any pair of nodes. As a result, sparsity describes o…

2019-10-03abs ↗pdf ↗

Gaussian and Non-Gaussian Universality of Data Augmentationcs.LG

Data augmentation affects estimates' uncertainty and distribution in complex ways.

problem Understanding how data augmentation impacts the variance and limiting distribution of estimates.

method Developed an adaptation of Lindeberg's technique for block dependence.

result Data augmentation can increase rather than decrease uncertainty, and it may shift the double-descent peak of an empirical risk.

data augmentation estimates uncertainty empirical risk double-descent

2022-02-18abs ↗pdf ↗

Pathwise CVA Regressions With Oversimulated Defaultsq-fin.CP

The paper develops a new simulation technique for estimating conditional expectations in financial models.

problem Estimating conditional expectations in financial models with expensive simulation of endogenous variables.

method Introduces a hierarchical simulation scheme with oversimplified defaults to address variance issues.

result The hierarchical simulation technique significantly improves the success of neural net regression for conditional expectation estimation.

conditional expectations simulation oversimplified defaults neural net regression graphics processing unit (GPU)

2022-11-30abs ↗pdf ↗

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentationmath.ST

Paper generalizes Gaussian universality and CGMT to dependent data, impacting data augmentation in high-dimensional logistic regression.

problem Limitation of Gaussian universality and CGMT in handling dependent data.

method Generalizes Gaussian universality and CGMT to dependent data (block dependence, m-dependence, mixing). Establishes a novel CGMT framework.

result Gaussian universality holds for high-dimensional logistic regression under various types of dependence.

logistic regression dependence Gaussian universality CGMT data augmentation

2025-02-10abs ↗pdf ↗

Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descentcs.LG

Sharp pseudospectral bounds prevent transient amplification in coupled gradient descent.

problem Transient amplification in coupled gradient descent systems.

method Developed a sharp pseudospectral theory for block-triangular Jacobians, proving Kreiss constant bounds and matching minimax lower bounds.

result Obtained a finite-horizon iteration-complexity bound of

O(K(J)^2 \log(1/δ))

for stochastic coupled descent.

coupled gradient descent transient amplification pseudospectral theory spectral radii Kreiss constant

2026-06-01abs ↗pdf ↗

Vector Copula Variational Inference and Dependent Block Posterior Approximationsstat.ML

This paper proposes a new method to improve VI approximations by capturing dependence between blocks using vector copulas.

problem Improving variational inference accuracy for complex models with challenging posteriors.

method Using vector copulas to model dependence between multivariate blocks, with learnable transport maps for flexible marginals.

result The proposed method produces more accurate posterior approximations than existing methods at limited computational cost.

variational inference vector copulas posterior approximation statistical models python package

2025-03-03abs ↗pdf ↗