@StatCOupdates

Unofficial account tweeting latest updates of statistical computation papers on arXiv

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Joined April 2019
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Stat.CO Papers retweeted
Omar Rivasplata
@OmarRivasplataMay 17
馃摐 Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation by David Holzm眉ller, Francis Bach 馃敆 arxiv.org/abs/2303.03237

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Sampling from Gibbs distributions and computing their log-partition function are fundamental tasks in statistics, machine learning, and statistical physics. While efficient algorithms are known...

arxiv.org
Stat.CO Papers@StatCOupdatesApr 24
David Holzm\"uller, Francis Bach. [statML]. Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation. (Replacement). arxiv.org/abs/2303.03237

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Sampling from Gibbs distributions and computing their log-partition function are fundamental tasks in statistics, machine learning, and statistical physics. While efficient algorithms are known...

arxiv.org
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3
356
Cornelius Fritz, Michael Schweinberger. [statCO]. R Package iglm: Regression under Interference in Connected Populations. arxiv.org/abs/2604.22791

R Package iglm: Regression under Interference in Connected Populations

We introduce R package iglm, which implements a comprehensive framework for studying relationships among predictors and outcomes under interference. The implemented regression framework...

arxiv.org
112
Johan Hallberg Szabadv\'ary. [statME]. A Fast, Closed-Form Bandwidth Selector for the Beta Kernel Density Estimator. (Replacement). arxiv.org/abs/2601.19553

A Fast, Closed-Form Bandwidth Selector for the Beta Kernel Density...

The Beta kernel estimator offers a theoretically superior alternative to the Gaussian kernel for unit interval data, eliminating boundary bias without requiring reflection or transformation....

arxiv.org
1
106
Shuhao Jiao, Hernando Ombao, Ian W. McKeague. [statME]. Filtration-Based Learning of Multiscale Shared Structures for Multiple Functional Predictors. (Replacement). arxiv.org/abs/2506.11369

Filtration-Based Learning of Multiscale Shared Structures for...

It is crucial to learn the shared structures among functional predictors, as these structures characterize how predictor components exert common effects and, more generally, how predictors are...

arxiv.org
1
78
Simon Hirsch. [statML]. Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting. (Replacement). arxiv.org/abs/2504.02518

Online Multivariate Regularized Distributional Regression for...

Probabilistic electricity price forecasting (PEPF) is vital for short-term electricity markets, yet the multivariate nature of day-ahead prices - spanning 24 consecutive hours - remains...

arxiv.org
1
55
Keiichiro Hijikata, Motonori Oka, Kensuke Okada. [statME]. Sparse Bayesian joint modal estimation for exploratory item factor analysis. (Replacement). arxiv.org/abs/2411.03992

Sparse Bayesian joint modal estimation for exploratory item factor analysis

This study presents a scalable Bayesian estimation algorithm for sparse estimation in exploratory item factor analysis based on a classical Bayesian estimation method, namely Bayesian joint modal...

arxiv.org
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61
Simon Hirsch, Jonathan Berrisch, Florian Ziel. [statML]. Online Distributional Regression. (Replacement). arxiv.org/abs/2407.08750

Online Distributional Regression

Large-scale streaming data are common in modern machine learning applications and have led to the development of online learning algorithms. Many fields, such as supply chain management, weather...

arxiv.org
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Xiaomei Yang, Jiaying Jia, Zhiliang Deng. [statCO]. A Bayesian approach with persistent homology prior for Robin coefficient identification in a parabolic problem. (Replacement). arxiv.org/abs/2512.24046

A Bayesian approach with persistent homology prior for Robin...

The reconstruction of time-dependent Robin coefficients is a challenging inverse heat transfer problem due to its inherent ill-posedness. This paper introduces a hierarchical Bayesian approach...

arxiv.org
1
69
Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez, F. Javier Rubio. [statML]. Conformalized Super Learner. arxiv.org/abs/2604.22391

Conformalized Super Learner

The Super Learner (SL) is a widely used ensemble method that combines point predictions from a library of learners based on their predictive performance. Interval predictions are of considerable...

arxiv.org
1
366
Maharani Ahsani Ummi, Stuart Barber, Henry M. Wood, Arief Gusnanto. [statAP]. Tail-Greedy Unbalanced Haar Wavelet Segmentation for Copy Number Alteration Data. arxiv.org/abs/2604.22364

Tail-Greedy Unbalanced Haar Wavelet Segmentation for Copy Number...

Detecting copy number alterations (CNAs) from next-generation sequencing data remains challenging, particularly for short segments under noisy conditions. Existing segmentation methods often...

arxiv.org
53
Chong Wang, Yanqi Zhang, Zhezhen Jin, Annette O'Connor. [statME]. Contrast-Space Projection for Network Meta-Analysis: An Exact and Invariant Study-Based Decomposition of Direct and Indirect Contributions. arxiv.org/abs/2604.21994

Contrast-Space Projection for Network Meta-Analysis: An Exact and...

Network meta-analysis (NMA) combines direct and indirect comparisons across a treatment network, but exact contribution decompositions that reproduce NMA estimates are lacking, especially for...

arxiv.org
52
Erik Sverdrup, Trevor Hastie. [statME]. balnet: Pathwise Estimation of Covariate Balancing Propensity Scores. (Replacement). arxiv.org/abs/2602.18577

balnet: Pathwise Estimation of Covariate Balancing Propensity Scores

We present balnet, an R package for scalable pathwise estimation of covariate balancing propensity scores via logistic covariate balancing loss functions. Regularization paths are computed with...

arxiv.org
64
Shuhao Jiao, Hernando Ombao, Ian W. McKeague. [statME]. Filtration-Based Learning of Multiscale Shared Structures for Multiple Functional Predictors. (Replacement). arxiv.org/abs/2506.11369

Filtration-Based Learning of Multiscale Shared Structures for...

It is crucial to learn the shared structures among functional predictors, as these structures characterize how predictor components exert common effects and, more generally, how predictors are...

arxiv.org
69
Jordan Awan, Andres Felipe Barrientos, Nianqiao Ju. [mathST]. Statistical Inference for Privatized Data with Unknown Sample Size. (Replacement). arxiv.org/abs/2406.06231

Statistical Inference for Privatized Data with Unknown Sample Size

We develop both theory and algorithms to analyze privatized data in unbounded differential privacy (DP), where even the sample size is considered a sensitive quantity that requires privacy...

arxiv.org
76
David Holzm\"uller, Francis Bach. [statML]. Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation. (Replacement). arxiv.org/abs/2303.03237

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Sampling from Gibbs distributions and computing their log-partition function are fundamental tasks in statistics, machine learning, and statistical physics. While efficient algorithms are known...

arxiv.org
1
4
449
Di Wu, Ling Liang, Haizhao Yang. [statML]. Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions. arxiv.org/abs/2604.21849
54
Franti\v{s}ek Barto\v{s}, Eric-Jan Wagenmakers, Maarten Marsman, Don van den Bergh. [statME]. Efficient Bayes Factor Sensitivity Analysis via Posterior Density Ratios. arxiv.org/abs/2604.21596

Efficient Bayes Factor Sensitivity Analysis via Posterior Density Ratios

Bayes factor sensitivity analysis examines how the evidence for one hypothesis over another depends on the prior distribution. In complex models, the standard approach refits the model at each...

arxiv.org
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Lena Zellinger, Antonio Vergari. [csLG]. Even More Guarantees for Variational Inference in the Presence of Symmetries. arxiv.org/abs/2604.21407

Even More Guarantees for Variational Inference in the Presence of...

When approximating an intractable density via variational inference (VI) the variational family is typically chosen as a simple parametric family that very likely does not contain the target. This...

arxiv.org
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Leo L Duan. [statME]. Revisiting Bayesian Variable Selection via Optimization. arxiv.org/abs/2604.21009

Revisiting Bayesian Variable Selection via Optimization

Variable selection in linear regression has been a central topic in statistical research for decades. Bayesian variable selection methods, which account for uncertainty in both the regression...

arxiv.org
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Utku Erdogan, Gabriel J. Lord, Joaquin Miguez. [statCO]. On a class of constrained particle filters for continuous-discrete state space models. arxiv.org/abs/2604.21538

On a class of constrained particle filters for continuous-discrete...

Particle filters (PFs) are recursive Monte Carlo algorithms for Bayesian tracking and prediction in state space models. This paper addresses continuous-discrete filtering problems, where the...

arxiv.org
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