Cover Image

Large Scale Partial Correlation Screening with Uncertainty Quantification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Neo, Emily, Radchenko, Peter, Rajaratnam, Bala
Format:	Preprint
Published:	2025
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Scalable and non-iterative graphical model estimation
by: Khare, Kshitij, et al.
Published: (2024)

Distributed Conditional Feature Screening via Pearson Partial Correlation with FDR Control
by: Pang, Naiwen, et al.
Published: (2024)

Multivariate and Online Transfer Learning with Uncertainty Quantification
by: Hickey, Jimmy, et al.
Published: (2024)

Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
by: Kosko, Matthew, et al.
Published: (2026)

Interventional Processes for Causal Uncertainty Quantification
by: Dance, Hugh, et al.
Published: (2024)

Uncertainty Quantification in Bayesian Clustering
by: Page, Garritt L., et al.
Published: (2025)

Optimal Forecast Reconciliation with Uncertainty Quantification
by: Møller, Jan Kloppenborg, et al.
Published: (2024)

Predictive Uncertainty Quantification with Missing Covariates
by: Zaffran, Margaux, et al.
Published: (2024)

Efficient Input Uncertainty Quantification for Ratio Estimator
by: He, Linyun, et al.
Published: (2024)

Prediction-Centric Uncertainty Quantification via MMD
by: Shen, Zheyang, et al.
Published: (2024)

Modeling with Categorical Features via Exact Fusion and Sparsity Regularisation
by: Behdin, Kayhan, et al.
Published: (2026)

Poisson Inventory Models with Many Items: An Empirical Bayes Approach
by: Anderson, Edward, et al.
Published: (2025)

Uncertainty Quantification With Multiple Sources
by: Ying, Mufang, et al.
Published: (2024)

Uncertainty Quantification for Ranking with Heterogeneous Preferences
by: Fan, Jianqing, et al.
Published: (2025)

Coherent Disaggregation and Uncertainty Quantification for Spatially Misaligned Data
by: Suen, Man Ho, et al.
Published: (2025)

Uncertainty Quantification in Bayesian Reduced-Rank Sparse Regressions
by: Pintado, Maria F., et al.
Published: (2023)

Ask for More Than Bayes Optimal: A Theory of Indecisions for Classification
by: Ndaoud, Mohamed, et al.
Published: (2024)

False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks
by: Machkour, Jasin, et al.
Published: (2024)

Bend to Mend: Toward Trustworthy Variational Bayes with Valid Uncertainty Quantification
by: Liu, Jiaming, et al.
Published: (2025)

Likelihood-free Posterior Density Learning for Uncertainty Quantification in Inference Problems
by: Zhang, Rui, et al.
Published: (2025)

Hierarchical Bayesian Modeling for Uncertainty Quantification and Reliability Updating using Data
by: Jia, Xinyu, et al.
Published: (2024)

Beyond fixed thresholds: optimizing summaries of wearable device data via piecewise linearization of quantile functions
by: Park, Junyoung, et al.
Published: (2025)

Deep Gaussian Process Emulation and Uncertainty Quantification for Large Computer Experiments
by: Yazdi, Faezeh, et al.
Published: (2024)

Uncertainty Quantification in Forecast Comparisons
by: Pohle, Marc-Oliver, et al.
Published: (2026)

Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
by: Wang, Yu, et al.
Published: (2026)

Statistically Optimal Uncertainty Quantification for Expensive Black-Box Models
by: He, Shengyi, et al.
Published: (2024)

Uncertainty Quantification using Simulation Output: Batching as an Inferential Device
by: Jeon, Yongseok, et al.
Published: (2023)

Multi-Layer Kernel Machines: Fast and Optimal Nonparametric Regression with Uncertainty Quantification
by: Dai, Xiaowu, et al.
Published: (2024)

Neural Conditional Probability for Uncertainty Quantification
by: Kostic, Vladimir R., et al.
Published: (2024)

BOB: Bayesian Optimized Bootstrap for Uncertainty Quantification in Gaussian Mixture Models
by: Marin, Santiago, et al.
Published: (2023)

Efficient Budget Allocation for Large-Scale LLM-Enabled Virtual Screening
by: Li, Zaile, et al.
Published: (2024)

Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations
by: Freidling, Tobias, et al.
Published: (2022)

Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
by: Burt, David R., et al.
Published: (2025)

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning
by: Trivedi, Shubhendu, et al.
Published: (2025)

Uncertainty Quantification and Confidence Intervals for Naive Rare-Event Estimators
by: Bai, Yuanlu, et al.
Published: (2023)

Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
by: Cattaneo, Matias D., et al.
Published: (2022)

Uncertainty Quantification of MLE for Entity Ranking with Covariates
by: Fan, Jianqing, et al.
Published: (2022)

Aitchison Geometry on the Simplex for Uncertainty Quantification in Bayesian Hyperspectral Image Unmixing
by: Blondel, Hector, et al.
Published: (2026)

Constructing Large Orthogonal Minimally Aliased Response Surface Designs by Concatenating Two Definitive Screening Designs
by: Vazquez, Alan R., et al.
Published: (2025)

Adaptive Uncertainty Quantification for Generative AI
by: Kim, Jungeum, et al.
Published: (2024)