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.

168,657 papers · 148 categories

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8 results for “covariate-specific”

Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Priorcs.LG

CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.

problem Inefficiency of global similarity measures in transfer learning for high-dimensional data.

method Conditional spike-and-slab prior with covariate-specific priors for robust partial information transfer.

result CONCERT achieves variable selection and information transfer simultaneously, improving performance on the target.

transfer learning partial information covariate-specific spike-and-slab prior variational Bayes

2024-03-30abs ↗pdf ↗

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regressionstat.ML

Nash integrates covariate-specific side info into sparse regression via neural networks.

problem Sparse linear regression struggles with covariates exhibiting structure or coming from heterogeneous sources.

method Neural Adaptive Shrinkage (Nash) framework that integrates side information into sparse regression via neural networks. Uses split variational empirical Bayes algorithm.

result Nash improves accuracy and adaptability over existing methods in real data experiments.

sparse regression neural networks covariate structure split variational empirical Bayes adaptive shrinkage

2025-05-16abs ↗pdf ↗

E-QRGMM: Efficient Generative Metamodeling for Covariate-Dependent Uncertainty Quantificationcs.LG

E-QRGMM accelerates uncertainty quantification in simulations.

problem Challenges in covariate-dependent uncertainty quantification.

method Integrates cubic Hermite interpolation with gradient estimation.

result Substantially improves computational efficiency and accuracy.

uncertainty quantification covariate-dependent quantile regression generative models bootstrap

2026-01-27abs ↗pdf ↗

BARTSIMP: flexible spatial covariate modeling and prediction using Bayesian additive regression treesstat.ME

Combines BART and Gaussian process for spatial covariate prediction with uncertainty.

problem Improving spatial prediction models with nonlinear and interaction covariates.

method Bayesian Additive Regression Trees (BART) combined with Gaussian process for spatial dependence.

result Effective in reducing computational burden through INLA and MCMC.

spatial statistics Bayesian Additive Regression Trees Gaussian process uncertainty estimation machine learning

2023-09-23abs ↗pdf ↗

ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regressionstat.ML

ADHAM provides interpretable survival analysis for healthcare.

problem Limited interpretability in deep learning survival models.

method Additive Deep Hazard Analysis Mixtures (ADHAM) with latent subgroup structure.

result ADHAM offers interpretable insights into exposure-outcome associations.

survival analysis interpretable models neural networks healthcare latent structure

2025-09-08abs ↗pdf ↗

Fed-TDA: Federated Tabular Data Augmentation on Non-IID Datacs.LG

Fed-TDA augments federated tabular data to improve performance and privacy.

problem Non-IID data challenges in federated learning.

method Synthesizes tabular data using simple statistics for augmentation.

result Fed-TDA improves test performance and communication efficiency.

federated learning tabular data data augmentation non-IID privacy

2022-11-22abs ↗pdf ↗

Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessmentphysics.geo-ph

New model predicts weekly earthquakes with better tail risk assessment.

problem Violation of Poisson assumption in seismic data.

method Neural network for per-cell overdispersion estimation.

result 8.6% reduction in mean pinball deviation, 12.5% lower CRPS in tail events.

seismic forecasting negative binomial regression earthquake prediction overdispersion tail risk

2026-05-20abs ↗pdf ↗

Multi forests: Variable importance for multi-class outcomesstat.ML

A new method identifies class-specific covariates in multi-class prediction tasks.

problem Identifying covariates specifically associated with one or more outcome classes in multi-class prediction tasks.

method Introducing multi forests (MuFs) with multi-way and binary splits to measure class-associated discriminatory ability.

result The multi-class VIM specifically ranks class-associated covariates highly, unlike conventional VIMs.

variable importance multi-class outcomes random forests multi-way splits binary splits

2024-09-13abs ↗pdf ↗