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Comparing Cluster-Based Cross-Validation Strategies for Machine Learning Model Evaluation
Afonso Martini Spezia, Mariana Recamonde-Mendoza
https://arxiv.org/abs/2507.22299 https…

Comparing Cluster-Based Cross-Validation Strategies for Machine Learning Model Evaluation
Cross-validation plays a fundamental role in Machine Learning, enabling robust evaluation of model performance and preventing overestimation on training and validation data. However, one of its drawbacks is the potential to create data subsets (folds) that do not adequately represent the diversity of the original dataset, which can lead to biased performance estimates. The objective of this work is to deepen the investigation of cluster-based cross-validation strategies by analyzing the perform…

Validating Generative Agent-Based Models of Social Norm Enforcement: From Replication to Novel Predictions
Logan Cross, Nick Haber, Daniel L. K. Yamins
https://arxiv.org/abs/2507.22049

Validating Generative Agent-Based Models of Social Norm Enforcement: From Replication to Novel Predictions
As large language models (LLMs) advance, there is growing interest in using them to simulate human social behavior through generative agent-based modeling (GABM). However, validating these models remains a key challenge. We present a systematic two-stage validation approach using social dilemma paradigms from psychological literature, first identifying the cognitive components necessary for LLM agents to reproduce known human behaviors in mixed-motive settings from two landmark papers, then usi…

Dimension Reduction for Conditional Density Estimation with Applications to High-Dimensional Causal Inference
Jianhua Mei, Fu Ouyang, Thomas T. Yang
https://arxiv.org/abs/2507.22312

Dimension Reduction for Conditional Density Estimation with Applications to High-Dimensional Causal Inference
We propose a novel and computationally efficient approach for nonparametric conditional density estimation in high-dimensional settings that achieves dimension reduction without imposing restrictive distributional or functional form assumptions. To uncover the underlying sparsity structure of the data, we develop an innovative conditional dependence measure and a modified cross-validation procedure that enables data-driven variable selection, thereby circumventing the need for subjective thresh…

Window observables for benchmarking parton distribution functions
Joe Karpie, Christopher J. Monahan, Kostas Orginos, Savvas Zafeiropoulos
https://arxiv.org/abs/2505.22795

Window observables for benchmarking parton distribution functions
Global analysis of collider and fixed-target experimental data and calculations from lattice quantum chromodynamics (QCD) are used to gain complementary information on the structure of hadrons. We propose novel ``window observables'' that allow for higher precision cross-validation between the different approaches, a critical step for studies that wish to combine the datasets. Global analyses are limited by the kinematic regions accessible to experiment, particularly in a range of Bjorken-$x$, …

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
Bokai Yang
https://arxiv.org/abs/2506.14244 https://

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
In the network literature, a wide range of statistical models have been proposed to exploit structural patterns in the data. Therefore, model selection between different models is a fundamental problem. Cross-validation is a powerful candidate to solve this problem, and Li et al. have already proposed an edge-sampling procedure to choose the number of communities in the block model framework. In this paper, we propose a penalized edge-sampling cross-validation framework for nested network model…

Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation
Steven C. Hespeler, Pablo Moriano, Mingyan Li, Samuel C. Hollifield
https://arxiv.org/abs/2506.12183

Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation
Evaluating anomaly detection in multivariate time series (MTS) requires careful consideration of temporal dependencies, particularly when detecting subsequence anomalies common in fault detection scenarios. While time series cross-validation (TSCV) techniques aim to preserve temporal ordering during model evaluation, their impact on classifier performance remains underexplored. This study systematically investigates the effect of TSCV strategy on the precision-recall characteristics of classifi…

Convergence of generalized cross-validation with applications to ill-posed integral equations
Tim Jahn, Mikhail Kirilin
https://arxiv.org/abs/2506.14558 ht…

Convergence of generalized cross-validation with applications to ill-posed integral equations
In this article, we rigorously establish the consistency of generalized cross-validation as a parameter-choice rule for solving inverse problems. We prove that the index chosen by leave-one-out GCV achieves a non-asymptotic, order-optimal error bound with high probability for polynomially ill-posed compact operators. Hereby it is remarkable that the unknown true solution need not satisfy a self-similarity condition, which is generally needed for other heuristic parameter choice rules. We quanti…

PrivacyXray: Detecting Privacy Breaches in LLMs through Semantic Consistency and Probability Certainty
Jinwen He, Yiyang Lu, Zijin Lin, Kai Chen, Yue Zhao
https://arxiv.org/abs/2506.19563

PrivacyXray: Detecting Privacy Breaches in LLMs through Semantic Consistency and Probability Certainty
Large Language Models (LLMs) are widely used in sensitive domains, including healthcare, finance, and legal services, raising concerns about potential private information leaks during inference. Privacy extraction attacks, such as jailbreaking, expose vulnerabilities in LLMs by crafting inputs that force the models to output sensitive information. However, these attacks cannot verify whether the extracted private information is accurate, as no public datasets exist for cross-validation, leaving…

The optimal Pad\'{e} polynomial for reconstruction of luminosity distance based on 10-fold cross-validation
Bo Yu, Wenhu Liu, XiaoFeng Yang, Tong-Jie zhang, Yanke Tang
https://arxiv.org/abs/2507.08695

The optimal Padé polynomial for reconstruction of luminosity distance based on 10-fold cross-validation
The cosmography known as the Padé polynomials has been widely used in the reconstruction of luminosity distance, and the orders of Padé polynomials influence the reconstructed result derived from Padé approximation. In this paper, we present a more general scheme of selecting optimal Padé polynomial for reconstruction of luminosity distance based on 10-fold cross-validation. Then the proposed scheme is applied to Pantheon+ dataset. The numerical results clearly indicate that the proposed pr…

High-Dimensional Model Averaging via Cross-Validation
Zhengyan Wan, Fang Fang, Binyan Jiang
https://arxiv.org/abs/2506.08451 https://…

High-Dimensional Model Averaging via Cross-Validation
Model averaging is an important alternative to model selection with attractive prediction accuracy. However, its application to high-dimensional data remains under-explored. We propose a high-dimensional model averaging method via cross-validation under a general framework and systematically establish its theoretical properties. Each candidate model is fitted using a flexible loss function paired with a general regularizer, and the optimal weights are determined by minimizing a cross-validation…

Highly accurate simulations of asymmetric black-hole scattering and cross validation of effective-one-body models
Oliver Long, Harald P. Pfeiffer, Alessandra Buonanno, Gustav Uhre Jakobsen, Gustav Mogull, Antoni Ramos-Buades, Hannes R. R\"uter, Lawrence E. Kidder, Mark A. Scheel
https://arxiv.org/abs/2507.08071

Highly accurate simulations of asymmetric black-hole scattering and cross validation of effective-one-body models
The study of unbound binary-black-hole encounters provides a gauge-invariant approach to exploring strong-field gravitational interactions in two-body systems, which can subsequently inform waveform models for bound orbits. In this work, we present 60 new highly accurate numerical relativity (NR) simulations of black-hole scattering, generated using the Spectral Einstein Code (SpEC). Our simulations include 14 spin-aligned configurations, as well as 16 configurations with unequal masses, up to …

The use of cross validation in the analysis of designed experiments
Maria L. Weese, Byran J. Smucker, David J. Edwards
https://arxiv.org/abs/2506.14593 htt…

The use of cross validation in the analysis of designed experiments
Cross-validation (CV) is a common method to tune machine learning methods and can be used for model selection in regression as well. Because of the structured nature of small, traditional experimental designs, the literature has warned against using CV in their analysis. The striking increase in the use of machine learning, and thus CV, in the analysis of experimental designs, has led us to empirically study the effectiveness of CV compared to other methods of selecting models in designed exper…

Sim2Real Diffusion: Learning Cross-Domain Adaptive Representations for Transferable Autonomous Driving
Chinmay Vilas Samak, Tanmay Vilas Samak, Bing Li, Venkat Krovi
https://arxiv.org/abs/2507.00236

Sim2Real Diffusion: Learning Cross-Domain Adaptive Representations for Transferable Autonomous Driving
Simulation-based design, optimization, and validation of autonomous driving algorithms have proven to be crucial for their iterative improvement over the years. Nevertheless, the ultimate measure of effectiveness is their successful transition from simulation to reality (sim2real). However, existing sim2real transfer methods struggle to comprehensively address the autonomy-oriented requirements of balancing: (i) conditioned domain adaptation, (ii) robust performance with limited examples, (iii)…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train…

Dual-Attention U-Net with Class-Specific Ensembles and Bayesian Hyperparameter Optimization for Precise Wound and Scale Marker Segmentation
Daniel Cie\'slak, Miriam Reca, Olena Onyshchenko, Jacek Rumi\'nski
https://arxiv.org/abs/2507.05314

Dual-Attention U-Net++ with Class-Specific Ensembles and Bayesian Hyperparameter Optimization for Precise Wound and Scale Marker Segmentation
Accurate segmentation of wounds and scale markers in clinical images remainsa significant challenge, crucial for effective wound management and automatedassessment. In this study, we propose a novel dual-attention U-Net++ archi-tecture, integrating channel-wise (SCSE) and spatial attention mechanisms toaddress severe class imbalance and variability in medical images effectively.Initially, extensive benchmarking across diverse architectures and encoders via 5-fold cross-validation identified Eff…

A Transfer Learning Framework for Multilayer Networks via Model Averaging
Yongqin Qiu, Xinyu Zhang
https://arxiv.org/abs/2506.12455 https://

A Transfer Learning Framework for Multilayer Networks via Model Averaging
Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting their practicality. To address these issues, we propose a novel transfer learning framework for multilayer networks using a bi-level model averaging method. A $K$-fold cross-validation criterion based on edg…

This https://arxiv.org/abs/2402.08151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Perturbative partial moment matching and gradient-flow adaptive importance sampling transformations for Bayesian leave one out cross-validation
Importance sampling (IS) allows one to approximate leave one out (LOO) cross-validation for a Bayesian model, without refitting, by inverting the Bayesian update equation to subtract a given data point from a model posterior. For each data point, one computes expectations under the corresponding LOO posterior by weighted averaging over the full data posterior. This task sometimes requires weight stabilization in the form of adapting the posterior distribution via transformation. So long as one …

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Afra Kilic, Kim Batselier
https://arxiv.org/abs/2507.11136 …

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model complexity hyperparameters like tensor rank and feature dimensions, often through trial-and-error or computationally costly methods like cross-validation. We propose Bayesian Tensor Network Kernel Machines, a fully probabil…