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Space evaluation at the starting point of soccer transitions arxiv.org/abs/2505.14711 .AP .AI

Stochastic Processes with Modified Lognormal Distribution Featuring Flexible Upper Tail arxiv.org/abs/2505.14713 .data-an .ME .ST .ML .TH .LG

Stochastic Processes with Modified Lognormal Distribution Featuring Flexible Upper Tail

Asymmetric, non-Gaussian probability distributions are often observed in the analysis of natural and engineering datasets. The lognormal distribution is a standard model for data with skewed frequency histograms and fat tails. However, the lognormal law severely restricts the asymptotic dependence of the probability density and the hazard function for high values. Herein we present a family of three-parameter non-Gaussian probability density functions that are based on generalized kappa-exponential and kappa-logarithm functions and investigate its mathematical properties. These kappa-lognormal densities represent continuous deformations of the lognormal with lighter right tails, controlled by the parameter kappa. In addition, bimodal distributions are obtained for certain parameter combinations. We derive closed-form analytic expressions for the main statistical functions of the kappa-lognormal distribution. For the moments, we derive bounds that are based on hypergeometric functions as well as series expansions. Explicit expressions for the gradient and Hessian of the negative log-likelihood are obtained to facilitate numerical maximum-likelihood estimates of the kappa-lognormal parameters from data. We also formulate a joint probability density function for kappa-lognormal stochastic processes by applying Jacobi's multivariate theorem to a latent Gaussian process. Estimation of the kappa-lognormal distribution based on synthetic and real data is explored. Furthermore, we investigate applications of kappa-lognormal processes with different covariance kernels in time series forecasting and spatial interpolation using warped Gaussian process regression. Our results are of practical interest for modeling skewed distributions in various scientific and engineering fields.

arXiv.org

Effective climate policies for major emission reductions of ozone precursors: Global evidence from two decades arxiv.org/abs/2505.14731 .AP .LG

Effective climate policies for major emission reductions of ozone precursors: Global evidence from two decades

Despite policymakers deploying various tools to mitigate emissions of ozone (O\textsubscript{3}) precursors, such as nitrogen oxides (NO\textsubscript{x}), carbon monoxide (CO), and volatile organic compounds (VOCs), the effectiveness of policy combinations remains uncertain. We employ an integrated framework that couples structural break detection with machine learning to pinpoint effective interventions across the building, electricity, industrial, and transport sectors, identifying treatment effects as abrupt changes without prior assumptions about policy treatment assignment and timing. Applied to two decades of global O\textsubscript{3} precursor emissions data, we detect 78, 77, and 78 structural breaks for NO\textsubscript{x}, CO, and VOCs, corresponding to cumulative emission reductions of 0.96-0.97 Gt, 2.84-2.88 Gt, and 0.47-0.48 Gt, respectively. Sector-level analysis shows that electricity sector structural policies cut NO\textsubscript{x} by up to 32.4\%, while in buildings, developed countries combined adoption subsidies with carbon taxes to achieve 42.7\% CO reductions and developing countries used financing plus fuel taxes to secure 52.3\%. VOCs abatement peaked at 38.5\% when fossil-fuel subsidy reforms were paired with financial incentives. Finally, hybrid strategies merging non-price measures (subsidies, bans, mandates) with pricing instruments delivered up to an additional 10\% co-benefit. These findings guide the sequencing and complementarity of context-specific policy portfolios for O\textsubscript{3} precursor mitigation.

arXiv.org

Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability arxiv.org/abs/2505.14850 .AP

Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability

Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that imposes a significant burden on healthcare systems. ICU readmissions among AP patients are common, especially in severe cases, with rates exceeding 40%. Identifying high-risk patients for readmission is crucial for improving outcomes. This study used the MIMIC-III database to identify ICU admissions for AP based on diagnostic codes. We applied a preprocessing pipeline including missing data imputation, correlation analysis, and hybrid feature selection. Recursive Feature Elimination with Cross-Validation (RFECV) and LASSO regression, supported by expert review, reduced over 50 variables to 20 key predictors, covering demographics, comorbidities, lab tests, and interventions. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE) in a five-fold cross-validation framework. We developed and optimized six machine learning models-Logistic Regression, k-Nearest Neighbors, Naive Bayes, Random Forest, LightGBM, and XGBoost-using grid search. Model performance was evaluated with AUROC, accuracy, F1 score, sensitivity, specificity, PPV, and NPV. XGBoost performed best, with an AUROC of 0.862 (95% CI: 0.800-0.920) and accuracy of 0.889 (95% CI: 0.858-0.923) on the test set. An ablation study showed that removing any feature decreased performance. SHAP analysis identified platelet count, age, and SpO2 as key predictors of readmission. This study shows that ensemble learning, informed feature selection, and handling class imbalance can improve ICU readmission prediction in AP patients, supporting targeted post-discharge interventions.

arXiv.org

LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks arxiv.org/abs/2505.14867 .ML .LG

LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks

Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by their high sensitivity to hyperparameter tuning and the absence of established methodologies for selecting the optimal models. To address these challenges, we propose LOBSTUR-GNN ({\bf Lo}cal {\bf B}oot{\bf s}trap for {\bf T}uning {\bf U}nsupervised {\bf R}epresentations in GNNs) i), a novel framework designed to adapt bootstrapping techniques for unsupervised graph representation learning. LOBSTUR-GNN tackles two main challenges: (a) adapting the bootstrap edge and feature resampling process to account for local graph dependencies in creating alternative versions of the same graph, and (b) establishing robust metrics for evaluating learned representations without ground-truth labels. Using locally bootstrapped resampling and leveraging Canonical Correlation Analysis (CCA) to assess embedding consistency, LOBSTUR provides a principled approach for hyperparameter tuning in unsupervised GNNs. We validate the effectiveness and efficiency of our proposed method through extensive experiments on established academic datasets, showing an 65.9\% improvement in the classification accuracy compared to an uninformed selection of hyperparameters. Finally, we deploy our framework on a real-world application, thereby demonstrating its validity and practical utility in various settings. \footnote{The code is available at \href{https://github.com/sowonjeong/lobstur-graph-bootstrap}{github.com/sowonjeong/lobstur-graph-bootstrap}.}

arXiv.org

Bayesian Multivariate Approach to Subnational mortality graduation with Age-Varying Smoothness arxiv.org/abs/2505.14955 .AP .ME .OT

Bayesian Multivariate Approach to Subnational mortality graduation with Age-Varying Smoothness

This work introduces a Bayesian smoothing approach for the joint graduation of mortality rates across multiple populations. In particular, dynamical linear models are used to induce smoothness across ages through structured dependence, analogously to how temporal correlation is accommodated in state-space time-indexed models. An essential issue in subnational mortality probabilistic modelling is the lack or sparseness of information for some subpopulations. For many countries, mortality data is severely limited, and approaches based on a single population model can result in high uncertainty in the adjusted mortality tables. Here, we recognize the interdependence within a group of mortality data and pursue the pooling of information across several curves that ideally share common characteristics, such as the influence of epidemics or major economic shifts. Our proposal considers multivariate Bayesian dynamical models with common parameters, allowing for borrowing of information across mortality tables and enabling tests of convergence across populations. We also employ discount factors, typical in DLMs, to regulate smoothness, with varying discounting across ages, ensuring less smoothness at younger ages and greater stability at adult ages. This setup implies a trade-off between stability and adaptability. The discount parameter controls the responsiveness of the fit at older ages to new data. The estimation is fully Bayesian, accommodating all uncertainties in modelling and prediction. To illustrate the effectiveness of our model, we analyse male and female mortality data from England and Wales between 2010 and 2012, obtained from the Office for National Statistics. In scenarios with simulated missing data, our approach showed strong performance and flexibility in pooling information from related populations with more complete data.

arXiv.org

Ancestry-Adjusted Polygenic Risk Scores for Predicting Obesity Risk in the Indonesian Population arxiv.org/abs/2505.13503 -bio.GN .ME

Ancestry-Adjusted Polygenic Risk Scores for Predicting Obesity Risk in the Indonesian Population

Obesity prevalence in Indonesian adults increased from 10.5% in 2007 to 23.4% in 2023. Studies showed that genetic predisposition significantly influences obesity susceptibility. To aid this, polygenic risk scores (PRS) help aggregate the effects of numerous genetic variants to assess genetic risk. However, 91% of genome-wide association studies (GWAS) involve European populations, limiting their applicability to Indonesians due to genetic diversity. This study aims to develop and validate an ancestry adjusted PRS for obesity in the Indonesian population using principal component analysis (PCA) method constructed from the 1000 Genomes Project data and our own genomic data from approximately 2,800 Indonesians. We calculate PRS for obesity using all races, then determine the first four principal components using ancestry-informative SNPs and develop a linear regression model to predict PRS based on these principal components. The raw PRS is adjusted by subtracting the predicted score to obtain an ancestry adjusted PRS for the Indonesian population. Our results indicate that the ancestry-adjusted PRS improves obesity risk prediction. Compared to the unadjusted PRS, the adjusted score improved classification performance with a 5% increase in area under the ROC curve (AUC). This approach underscores the importance of population-specific adjustments in genetic risk assessments to enable more effective personalized healthcare and targeted intervention strategies for diverse populations.

arXiv.org

Data Balancing Strategies: A Survey of Resampling and Augmentation Methods arxiv.org/abs/2505.13518 .ML .AI .LG

Data Balancing Strategies: A Survey of Resampling and Augmentation Methods

Imbalanced data poses a significant obstacle in machine learning, as an unequal distribution of class labels often results in skewed predictions and diminished model accuracy. To mitigate this problem, various resampling strategies have been developed, encompassing both oversampling and undersampling techniques aimed at modifying class proportions. Conventional oversampling approaches like SMOTE enhance the representation of the minority class, whereas undersampling methods focus on trimming down the majority class. Advances in deep learning have facilitated the creation of more complex solutions, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which are capable of producing high-quality synthetic examples. This paper reviews a broad spectrum of data balancing methods, classifying them into categories including synthetic oversampling, adaptive techniques, generative models, ensemble-based strategies, hybrid approaches, undersampling, and neighbor-based methods. Furthermore, it highlights current developments in resampling techniques and discusses practical implementations and case studies that validate their effectiveness. The paper concludes by offering perspectives on potential directions for future exploration in this domain.

arXiv.org

Continuous Domain Generalization arxiv.org/abs/2505.13519 .ML .AI .LG

Continuous Domain Generalization

Real-world data distributions often shift continuously across multiple latent factors such as time, geography, and socioeconomic context. However, existing domain generalization approaches typically treat domains as discrete or evolving along a single axis (e.g., time), which fails to capture the complex, multi-dimensional nature of real-world variation. This paper introduces the task of Continuous Domain Generalization (CDG), which aims to generalize predictive models to unseen domains defined by arbitrary combinations of continuous variation descriptors. We present a principled framework grounded in geometric and algebraic theory, showing that optimal model parameters across domains lie on a low-dimensional manifold. To model this structure, we propose a Neural Lie Transport Operator (NeuralLTO), which enables structured parameter transitions by enforcing geometric continuity and algebraic consistency. To handle noisy or incomplete domain descriptors, we introduce a gating mechanism to suppress irrelevant dimensions and a local chart-based strategy for robust generalization. Extensive experiments on synthetic and real-world datasets-including remote sensing, scientific documents, and traffic forecasting-demonstrate that our method significantly outperforms existing baselines in generalization accuracy and robustness under descriptor imperfections.

arXiv.org

Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback arxiv.org/abs/2505.13562 .ML .AI .GT .LG .NE

Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback

Learning in games is a fundamental problem in machine learning and artificial intelligence, with numerous applications~\citep{silver2016mastering,schrittwieser2020mastering}. This work investigates two-player zero-sum matrix games with an unknown payoff matrix and bandit feedback, where each player observes their actions and the corresponding noisy payoff. Prior studies have proposed algorithms for this setting~\citep{o2021matrix,maiti2023query,cai2024uncoupled}, with \citet{o2021matrix} demonstrating the effectiveness of deterministic optimism (e.g., \ucb) in achieving sublinear regret. However, the potential of randomised optimism in matrix games remains theoretically unexplored. We propose Competitive Co-evolutionary Bandit Learning (\coebl), a novel algorithm that integrates evolutionary algorithms (EAs) into the bandit framework to implement randomised optimism through EA variation operators. We prove that \coebl achieves sublinear regret, matching the performance of deterministic optimism-based methods. To the best of our knowledge, this is the first theoretical regret analysis of an evolutionary bandit learning algorithm in matrix games. Empirical evaluations on diverse matrix game benchmarks demonstrate that \coebl not only achieves sublinear regret but also consistently outperforms classical bandit algorithms, including \exptr~\citep{auer2002nonstochastic}, the variant \exptrni~\citep{cai2024uncoupled}, and \ucb~\citep{o2021matrix}. These results highlight the potential of evolutionary bandit learning, particularly the efficacy of randomised optimism via evolutionary algorithms in game-theoretic settings.

arXiv.org

Minimax Rates of Estimation for Optimal Transport Map between Infinite-Dimensional Spaces arxiv.org/abs/2505.13570 .ST .ML .TH

Minimax Rates of Estimation for Optimal Transport Map between Infinite-Dimensional Spaces

We investigate the estimation of an optimal transport map between probability measures on an infinite-dimensional space and reveal its minimax optimal rate. Optimal transport theory defines distances within a space of probability measures, utilizing an optimal transport map as its key component. Estimating the optimal transport map from samples finds several applications, such as simulating dynamics between probability measures and functional data analysis. However, some transport maps on infinite-dimensional spaces require exponential-order data for estimation, which undermines their applicability. In this paper, we investigate the estimation of an optimal transport map between infinite-dimensional spaces, focusing on optimal transport maps characterized by the notion of $γ$-smoothness. Consequently, we show that the order of the minimax risk is polynomial rate in the sample size even in the infinite-dimensional setup. We also develop an estimator whose estimation error matches the minimax optimal rate. With these results, we obtain a class of reasonably estimable optimal transport maps on infinite-dimensional spaces and a method for their estimation. Our experiments validate the theory and practical utility of our approach with application to functional data analysis.

arXiv.org

Backward Conformal Prediction arxiv.org/abs/2505.13732 .ML .LG

Backward Conformal Prediction

We introduce $\textit{Backward Conformal Prediction}$, a method that guarantees conformal coverage while providing flexible control over the size of prediction sets. Unlike standard conformal prediction, which fixes the coverage level and allows the conformal set size to vary, our approach defines a rule that constrains how prediction set sizes behave based on the observed data, and adapts the coverage level accordingly. Our method builds on two key foundations: (i) recent results by Gauthier et al. [2025] on post-hoc validity using e-values, which ensure marginal coverage of the form $\mathbb{P}(Y_{\rm test} \in \hat C_n^{\tildeα}(X_{\rm test})) \ge 1 - \mathbb{E}[\tildeα]$ up to a first-order Taylor approximation for any data-dependent miscoverage $\tildeα$, and (ii) a novel leave-one-out estimator $\hatα^{\rm LOO}$ of the marginal miscoverage $\mathbb{E}[\tildeα]$ based on the calibration set, ensuring that the theoretical guarantees remain computable in practice. This approach is particularly useful in applications where large prediction sets are impractical such as medical diagnosis. We provide theoretical results and empirical evidence supporting the validity of our method, demonstrating that it maintains computable coverage guarantees while ensuring interpretable, well-controlled prediction set sizes.

arXiv.org

Finding Distributions that Differ, with False Discovery Rate Control arxiv.org/abs/2505.13769 .ME

Finding Distributions that Differ, with False Discovery Rate Control

We consider the problem of comparing a reference distribution with several other distributions. Given a sample from both the reference and the comparison groups, we aim to identify the comparison groups whose distributions differ from that of the reference group. Viewing this as a multiple testing problem, we introduce a methodology that provides exact, distribution-free control of the false discovery rate. To do so, we introduce the concept of batch conformal p-values and demonstrate that they satisfy positive regression dependence across the groups [Benjamini and Yekutieli, 2001], thereby enabling control of the false discovery rate through the Benjamini-Hochberg procedure. The proof of positive regression dependence introduces a novel technique for the inductive construction of rank vectors with almost sure dominance under exchangeability. We evaluate the performance of the proposed procedure through simulations, where, despite being distribution-free, in some cases they show performance comparable to methods with knowledge of the data-generating normal distribution; and further have more power than direct approaches based on conformal out-of-distribution detection. Further, we illustrate our methods on a Hepatitis C treatment dataset, where they can identify patient groups with large treatment effects; and on the Current Population Survey dataset, where they can identify sub-population with long work hours.

arXiv.org

A Bayesian Sparse Kronecker Product Decomposition Framework for Tensor Predictors with Mixed-Type Responses arxiv.org/abs/2505.13821 .ME

A Bayesian Sparse Kronecker Product Decomposition Framework for Tensor Predictors with Mixed-Type Responses

Ultra-high-dimensional tensor predictors are increasingly common in neuroimaging and other biomedical studies, yet existing methods rarely integrate continuous, count, and binary responses in a single coherent model. We present a Bayesian Sparse Kronecker Product Decomposition (BSKPD) that represents each regression (or classification) coefficient tensor as a low-rank Kronecker product whose factors are endowed with element-wise Three-Parameter Beta-Normal shrinkage priors, yielding voxel-level sparsity and interpretability. Embedding Gaussian, Poisson, and Bernoulli outcomes in a unified exponential-family form, and combining the shrinkage priors with Polya-Gamma data augmentation, gives closed-form Gibbs updates that scale to full-resolution 3-D images. We prove posterior consistency and identifiability even when each tensor mode dimension grows subexponentially with the sample size, thereby extending high-dimensional Bayesian theory to mixed-type multivariate responses. Simulations and applications to ADNI and OASIS magnetic-resonance imaging datasets show that BSKPD delivers sharper signal recovery and lower predictive error than current low-rank or sparsity-only competitors while preserving scientific interpretability.

arXiv.org
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