The paper proposes using modified Complementary Cumulative Distribution Function (mCCDF) plots to visualize and communicate results of cumulative-link ordinal regression models for ordinal data.

This paper introduces Distributional Random Forests, which replace mean-based CART splitting with criteria that compare full conditional response distributions in candidate children. The authors syste…

This paper proposes a conceptual framework for digital vehicle forensics (DVF) investigation, addressing the challenges of identifying, preserving, and acquiring digital evidence from modern vehicles.

This paper proposes a Bayesian framework to enhance membership inference attacks against released statistics by incorporating prior knowledge about the population's attribute dependency structure, out…

This paper introduces a new R package for Non-negative Matrix Factorization (NMF) and compares its performance systematically with two other R packages using real-world data.

The paper introduces and analyzes several novel data appraisal metrics, including the Vendi Score and matrix spectral functions, demonstrating that efficient optimization techniques make these metrics…

The paper proposes a novel two-stage framework to differentially privatize tables of counts by focusing on preserving the accuracy of the underlying count distribution, introducing the specialized cyc…

The paper introduces Score Broadcast and Decorrelation (SBD), a general theoretical framework that unifies broadcast-based credit assignment across various differentiable loss functions by leveraging…

The paper proposes MVRAF, a data-driven framework that quantifies vulnerability risk in large-scale cloud infrastructure by integrating multiple attack attributes and analyzing cumulative risk distrib…

The paper proposes a novel, perfectly secure Information-Theoretic Distributed Point Function (ITDPF) that converts point functions into shares using asymptotically shorter secret keys compared to exi…

This paper establishes a connection between neural network approximation of score functions and approximation of probability distributions generated by reverse diffusion models.

The paper introduces the linear canonical Riesz potential (LCRP) and analyzes its convergence properties, leveraging these findings to propose a novel, secure, and efficient asymmetric cascaded LCRP m…