Statistics

A look at how harmonic analysis helps researchers study stars and their oscillations.

New approach combines traditional methods to measure policy impacts.

Exploring fairness issues in AI language models and their implications.

New algorithm improves clustering by combining features and spatial positions.

Scientists enhance solar energy predictions using bias correction methods for climate models.

A new model enhances clinical outcome predictions for patients with Multiple Myeloma.

A new approach to boxplots ensures data privacy while maintaining insightful analysis.

A look at how companies in Europe are rated on sustainability.

A new method estimates parameters of neuron activity using real data.

We develop methods for refining models using noisy and indirect observations.

This work improves sampling methods for complex systems stuck in certain states.

GMR offers a fresh approach to studying multiple factors and outcomes in research.

A look at how companies in Europe are rated on sustainability.

Learn how Bayesian online learning adapts models with incoming data.

A new model enhances the analysis of complex health data.

New tools improve efficiency using lower precision in computations for research.

A deep dive into U-calibration error and its role in predicting outcomes.

A new approach to enhance model safety through prediction rejection.

This article discusses how AI models learn from mistakes through self-correction.

Learn how to navigate interference in data-driven decisions.

New method improves reliability in measuring uncertainty in predictive models.

A look at the role of complexity in model performance.

A novel loss function enhances feature learning in classification tasks.

Exploring classification methods for overlapping Gaussian mixtures in machine learning.

A new framework improves clustering accuracy for event data with outliers.

Introducing watermarking to protect counterfactual explanations in machine learning.

Learn how to navigate interference in data-driven decisions.

New method improves reliability in measuring uncertainty in predictive models.

A flexible approach for generating CFEs that respects data privacy concerns.

Examining spatial data patterns and their information content.

Tensors offer a new way to analyze complex data across various fields.

A new method enhances accuracy of treatment effects across diverse patient groups.

A method allowing adaptive statistical analysis without rigid model assumptions.

A new approach to boxplots ensures data privacy while maintaining insightful analysis.

This research develops methods for fair resource allocation despite limited user feedback.

A look into how the estimand framework improves clinical trial analysis.

A fresh approach to understanding complex human connections through fuzzy social network analysis.

A study on how Park-and-Ride can reduce congestion and emissions.

Information significantly impacts decision-making processes across various fields.

Benford's Law reveals odd patterns in first digits across data sets.

A new method enhances covariance estimation in Gaussian processes with adaptive thresholding.

Tensors offer a new way to analyze complex data across various fields.

Examining the influence of noise and structured signals in random matrix models.

Learn how transfer learning aids in classifying dynamic data effectively.

New algorithms for extracting signals from complex noise scenarios.

This paper explores EM algorithm behavior in mixed linear regression for improved model accuracy.

Exploring improvements in predicting point patterns using Point Process Learning.

Explore methods for handling sparse high-dimensional data in regression analysis.