mailitics

Category: stat.ME

  • Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents

    Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents arXiv:2603.03401v1 Announce Type: new Abstract: This paper proposes a novel parameter selection strategy for kernel-based gradient descent (KGD) algorithms, integrating bias-variance analysis with the splitting method. We introduce the concept of empirical effective dimension to quantify iteration increments in KGD, deriving an adaptive parameter selection strategy…

  • Learning with the Nash-Sutcliffe loss

    Learning with the Nash-Sutcliffe loss arXiv:2603.00968v1 Announce Type: new Abstract: The Nash-Sutcliffe efficiency ($text{NSE}$) is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decision-theoretic foundation for this purpose. To address this, we examine its negatively oriented counterpart, which we refer to as Nash-Sutcliffe loss, defined…

  • Multivariate Spatio-Temporal Neural Hawkes Processes

    Multivariate Spatio-Temporal Neural Hawkes Processes arXiv:2602.23629v1 Announce Type: new Abstract: We propose a Multivariate Spatio-Temporal Neural Hawkes Process for modeling complex multivariate event data with spatio-temporal dynamics. The proposed model extends continuous-time neural Hawkes processes by integrating spatial information into latent state evolution through learned temporal and spatial decay dynamics, enabling flexible modeling of excitation…

  • Efficient Inference after Directionally Stable Adaptive Experiments

    Efficient Inference after Directionally Stable Adaptive Experiments arXiv:2602.21478v1 Announce Type: new Abstract: We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, directional stability, which is strictly weaker than previously imposed target-agnostic stability conditions. Under directional stability, we show that estimators that…

  • Amortized Bayesian inference for actigraph time sheet data from mobile devices

    Amortized Bayesian inference for actigraph time sheet data from mobile devices arXiv:2602.20611v1 Announce Type: new Abstract: Mobile data technologies use “actigraphs” to furnish information on health variables as a function of a subject’s movement. The advent of wearable devices and related technologies has propelled the creation of health databases consisting of human movement data to…

  • Multiclass Calibration Assessment and Recalibration of Probability Predictions via the Linear Log Odds Calibration Function

    Multiclass Calibration Assessment and Recalibration of Probability Predictions via the Linear Log Odds Calibration Function arXiv:2602.18573v1 Announce Type: new Abstract: Machine-generated probability predictions are essential in modern classification tasks such as image classification. A model is well calibrated when its predicted probabilities correspond to observed event frequencies. Despite the need for multicategory recalibration methods, existing…

  • Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

    Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models arXiv:2602.16061v1 Announce Type: new Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard…

  • Locally Private Parametric Methods for Change-Point Detection

    Locally Private Parametric Methods for Change-Point Detection arXiv:2602.13619v1 Announce Type: new Abstract: We study parametric change-point detection, where the goal is to identify distributional changes in time series, under local differential privacy. In the non-private setting, we derive improved finite-sample accuracy guarantees for a change-point detection algorithm based on the generalized log-likelihood ratio test, via…

  • Amortised and provably-robust simulation-based inference

    Amortised and provably-robust simulation-based inference arXiv:2602.11325v1 Announce Type: new Abstract: Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other extreme values in data which occur due to faulty measurement instruments or human error. In this paper,…

  • Provable Offline Reinforcement Learning for Structured Cyclic MDPs

    Provable Offline Reinforcement Learning for Structured Cyclic MDPs arXiv:2602.11679v1 Announce Type: new Abstract: We introduce a novel cyclic Markov decision process (MDP) framework for multi-step decision problems with heterogeneous stage-specific dynamics, transitions, and discount factors across the cycle. In this setting, offline learning is challenging: optimizing a policy at any stage shifts the state distributions…

  • Causal Effect Estimation with Learned Instrument Representations

    Causal Effect Estimation with Learned Instrument Representations arXiv:2602.10370v1 Announce Type: new Abstract: Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of a valid instrument, which can often be difficult or infeasible to identify in practice. In this paper, we propose a representation learning approach that…

  • Flow-Based Conformal Predictive Distributions

    Flow-Based Conformal Predictive Distributions arXiv:2602.07633v1 Announce Type: new Abstract: Conformal prediction provides a distribution-free framework for uncertainty quantification via prediction sets with exact finite-sample coverage. In low dimensions these sets are easy to interpret, but in high-dimensional or structured output spaces they are difficult to represent and use, which can limit their ability to integrate…

  • Radon–Wasserstein Gradient Flows for Interacting-Particle Sampling in High Dimensions

    Radon–Wasserstein Gradient Flows for Interacting-Particle Sampling in High Dimensions arXiv:2602.05227v1 Announce Type: new Abstract: Gradient flows of the Kullback–Leibler (KL) divergence, such as the Fokker–Planck equation and Stein Variational Gradient Descent, evolve a distribution toward a target density known only up to a normalizing constant. We introduce new gradient flows of the KL divergence with…

  • Learning Multi-type heterogeneous interacting particle systems

    Learning Multi-type heterogeneous interacting particle systems arXiv:2602.03954v1 Announce Type: new Abstract: We propose a framework for the joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems from multi-trajectory data. This learning task is a challenging non-convex mixed-integer optimization problem, which we address through a novel three-stage approach.…

  • Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models

    Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models arXiv:2601.22336v1 Announce Type: new Abstract: Large-scale AI evaluation increasingly relies on aggregating binary judgments from $K$ annotators, including LLMs used as judges. Most classical methods, e.g., Dawid-Skene or (weighted) majority voting, assume annotators are conditionally independent given the true label $Yin{0,1}$, an assumption often violated by LLM…

  • Empirical Likelihood-Based Fairness Auditing: Distribution-Free Certification and Flagging

    Empirical Likelihood-Based Fairness Auditing: Distribution-Free Certification and Flagging arXiv:2601.20269v1 Announce Type: new Abstract: Machine learning models in high-stakes applications, such as recidivism prediction and automated personnel selection, often exhibit systematic performance disparities across sensitive subpopulations, raising critical concerns regarding algorithmic bias. Fairness auditing addresses these risks through two primary functions: certification, which verifies adherence to…

  • Data-Driven Information-Theoretic Causal Bounds under Unmeasured Confounding

    Data-Driven Information-Theoretic Causal Bounds under Unmeasured Confounding arXiv:2601.17160v1 Announce Type: new Abstract: We develop a data-driven information-theoretic framework for sharp partial identification of causal effects under unmeasured confounding. Existing approaches often rely on restrictive assumptions, such as bounded or discrete outcomes; require external inputs (for example, instrumental variables, proxies, or user-specified sensitivity parameters); necessitate full…

  • Perfect Clustering for Sparse Directed Stochastic Block Models

    Perfect Clustering for Sparse Directed Stochastic Block Models arXiv:2601.16427v1 Announce Type: new Abstract: Exact recovery in stochastic block models (SBMs) is well understood in undirected settings, but remains considerably less developed for directed and sparse networks, particularly when the number of communities diverges. Spectral methods for directed SBMs often lack stability in asymmetric, low-degree regimes,…

  • Robust X-Learner: Breaking the Curse of Imbalance and Heavy Tails via Robust Cross-Imputation

    Robust X-Learner: Breaking the Curse of Imbalance and Heavy Tails via Robust Cross-Imputation arXiv:2601.15360v1 Announce Type: new Abstract: Estimating Heterogeneous Treatment Effects (HTE) in industrial applications such as AdTech and healthcare presents a dual challenge: extreme class imbalance and heavy-tailed outcome distributions. While the X-Learner framework effectively addresses imbalance through cross-imputation, we demonstrate that it…

  • Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add

    Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add arXiv:2601.16120v1 Announce Type: new Abstract: Imbalanced classification, where one class is observed far less frequently than the other, often causes standard training procedures to prioritize the majority class and perform poorly on rare but important cases. A classic and…

  • Semi-Supervised Mixture Models under the Concept of Missing at Radom with Margin Confidence and Aranda Ordaz Function

    Semi-Supervised Mixture Models under the Concept of Missing at Radom with Margin Confidence and Aranda Ordaz Function arXiv:2601.14631v1 Announce Type: new Abstract: This paper presents a semi-supervised learning framework for Gaussian mixture modelling under a Missing at Random (MAR) mechanism. The method explicitly parameterizes the missingness mechanism by modelling the probability of missingness as a…

  • Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis

    Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis arXiv:2601.11790v1 Announce Type: new Abstract: Global sensitivity analysis of complex numerical simulators is often limited by the small number of model evaluations that can be afforded. In such settings, surrogate models built from a limited set of simulations can substantially reduce the computational burden, provided…

  • MLCBART: Multilabel Classification with Bayesian Additive Regression Trees

    MLCBART: Multilabel Classification with Bayesian Additive Regression Trees arXiv:2601.08964v1 Announce Type: cross Abstract: Multilabel Classification (MLC) deals with the simultaneous classification of multiple binary labels. The task is challenging because, not only may there be arbitrarily different and complex relationships between predictor variables and each label, but associations among labels may exist even after accounting…

  • Robust low-rank estimation with multiple binary responses using pairwise AUC loss

    Robust low-rank estimation with multiple binary responses using pairwise AUC loss arXiv:2601.08618v1 Announce Type: new Abstract: Multiple binary responses arise in many modern data-analytic problems. Although fitting separate logistic regressions for each response is computationally attractive, it ignores shared structure and can be statistically inefficient, especially in high-dimensional and class-imbalanced regimes. Low-rank models offer a…

  • Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies

    Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies arXiv:2601.06782v1 Announce Type: new Abstract: Individualized treatment regimes (ITRs) aim to improve clinical outcomes by assigning treatment based on patient-specific characteristics. However, existing methods often struggle with high-dimensional covariates, limiting accuracy, interpretability, and real-world applicability. We propose a novel sufficient dimension reduction approach that…

  • A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference

    A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference arXiv:2601.05355v1 Announce Type: new Abstract: Modern data analysis increasingly requires flexible conditional inference P(X_B | X_A) where (X_A, X_B) is an arbitrary partition of observed variable X. Existing conditional inference methods lack this flexibility as they are tied to a fixed conditioning structure and cannot perform…

  • Deep learning estimation of the spectral density of functional time series on large domains

    Deep learning estimation of the spectral density of functional time series on large domains arXiv:2601.00284v1 Announce Type: cross Abstract: We derive an estimator of the spectral density of a functional time series that is the output of a multilayer perceptron neural network. The estimator is motivated by difficulties with the computation of existing spectral density…

  • Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach

    Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach arXiv:2601.00287v1 Announce Type: cross Abstract: The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational studies, multiple versions may exist in the treatment.…

  • Likelihood-Preserving Embeddings for Statistical Inference

    Likelihood-Preserving Embeddings for Statistical Inference arXiv:2512.22638v1 Announce Type: new Abstract: Modern machine learning embeddings provide powerful compression of high-dimensional data, yet they typically destroy the geometric structure required for classical likelihood-based statistical inference. This paper develops a rigorous theory of likelihood-preserving embeddings: learned representations that can replace raw data in likelihood-based workflows — hypothesis testing,…

  • Diffusion Models in Simulation-Based Inference: A Tutorial Review

    Diffusion Models in Simulation-Based Inference: A Tutorial Review arXiv:2512.20685v1 Announce Type: new Abstract: Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. Their score-based formulation offers a flexible way to learn conditional or joint distributions over parameters and observations,…

  • Semiparametric KSD test: unifying score and distance-based approaches for goodness-of-fit testing

    Semiparametric KSD test: unifying score and distance-based approaches for goodness-of-fit testing arXiv:2512.20007v1 Announce Type: new Abstract: Goodness-of-fit (GoF) tests are fundamental for assessing model adequacy. Score-based tests are appealing because they require fitting the model only once under the null. However, extending them to powerful nonparametric alternatives is difficult due to the lack of suitable…

  • Sharp Structure-Agnostic Lower Bounds for General Functional Estimation

    Sharp Structure-Agnostic Lower Bounds for General Functional Estimation arXiv:2512.17341v1 Announce Type: new Abstract: The design of efficient nonparametric estimators has long been a central problem in statistics, machine learning, and decision making. Classical optimal procedures often rely on strong structural assumptions, which can be misspecified in practice and complicate deployment. This limitation has sparked growing…

  • Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics

    Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics arXiv:2512.13997v1 Announce Type: new Abstract: Existing two-sample testing techniques, particularly those based on choosing a kernel for the Maximum Mean Discrepancy (MMD), often assume equal sample sizes from the two distributions. Applying these methods in practice can require discarding valuable data, unnecessarily reducing test power.…

  • On the Hardness of Conditional Independence Testing In Practice

    On the Hardness of Conditional Independence Testing In Practice arXiv:2512.14000v1 Announce Type: new Abstract: Tests of conditional independence (CI) underpin a number of important problems in machine learning and statistics, from causal discovery to evaluation of predictor fairness and out-of-distribution robustness. Shah and Peters (2020) showed that, contrary to the unconditional case, no universally finite-sample…

  • Towards a pretrained deep learning estimator of the Linfoot informational correlation

    Towards a pretrained deep learning estimator of the Linfoot informational correlation arXiv:2512.12358v1 Announce Type: new Abstract: We develop a supervised deep-learning approach to estimate mutual information between two continuous random variables. As labels, we use the Linfoot informational correlation, a transformation of mutual information that has many important properties. Our method is based on ground…

  • Provable Recovery of Locally Important Signed Features and Interactions from Random Forest

    Provable Recovery of Locally Important Signed Features and Interactions from Random Forest arXiv:2512.11081v1 Announce Type: new Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction models. In many domains, such as personalized medicine, local interpretations for individual predictions are…

  • WTNN: Weibull-Tailored Neural Networks for survival analysis

    WTNN: Weibull-Tailored Neural Networks for survival analysis arXiv:2512.09163v1 Announce Type: new Abstract: The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to express the distribution’s parameters as functions of time-dependent covariates. Deep neural…

  • Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders

    Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders arXiv:2512.06348v1 Announce Type: new Abstract: Extreme weather events are widely studied in fields such as agriculture, ecology, and meteorology. The spatio-temporal co-occurrence of extreme events can strengthen or weaken under changing climate conditions. In this paper, we propose a novel approach to model spatio-temporal extremes by integrating climate…

  • Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches

    Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches arXiv:2512.05611v1 Announce Type: new Abstract: We study the calibration of Gaussian process (GP) predictive distributions in the interpolation setting from a design-marginal perspective. Conditioning on the data and averaging over a design measure mu, we formalize mu-coverage for central intervals and mu-probabilistic calibration through…

  • Colored Markov Random Fields for Probabilistic Topological Modeling

    Colored Markov Random Fields for Probabilistic Topological Modeling arXiv:2512.03727v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint distribution into lower-dimensional components. This makes PGMs well-suited for analyzing complex systems and supporting decision-making. Recent advances in topological…

  • A Sensitivity Approach to Causal Inference Under Limited Overlap

    A Sensitivity Approach to Causal Inference Under Limited Overlap arXiv:2511.22003v1 Announce Type: new Abstract: Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance weights can reduce variance but introduce a fundamental bias. We propose a sensitivity framework for contextualizing findings under limited overlap, where we…

  • Geometric Calibration and Neutral Zones for Uncertainty-Aware Multi-Class Classification

    Geometric Calibration and Neutral Zones for Uncertainty-Aware Multi-Class Classification arXiv:2511.20960v1 Announce Type: new Abstract: Modern artificial intelligence systems make critical decisions yet often fail silently when uncertain. We develop a geometric framework for post-hoc calibration of neural network probability outputs, treating probability vectors as points on the $(c-1)$-dimensional probability simplex equipped with the Fisher–Rao metric.…

  • Clustering Approaches for Mixed-Type Data: A Comparative Study

    Clustering Approaches for Mixed-Type Data: A Comparative Study arXiv:2511.19755v1 Announce Type: new Abstract: Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suited for this task. This study presents the state-of-the-art of these approaches and compares…

  • An operator splitting analysis of Wasserstein–Fisher–Rao gradient flows

    An operator splitting analysis of Wasserstein–Fisher–Rao gradient flows arXiv:2511.18060v1 Announce Type: new Abstract: Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows. Existing algorithmic developments implicitly make use of operator splitting techniques to numerically approximate the WFR…

  • Convex Clustering Redefined: Robust Learning with the Median of Means Estimator

    Convex Clustering Redefined: Robust Learning with the Median of Means Estimator arXiv:2511.14784v1 Announce Type: new Abstract: Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like k-means and its wide family of variants are still widely used, all of them require the…

  • PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure

    PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure arXiv:2511.07997v1 Announce Type: new Abstract: We revisit the problem of generating synthetic data under differential privacy. To address the core limitations of marginal-based methods, we propose the Private Adaptive Generative Adversarial Network with Bayes Network Structure (PrAda-GAN), which integrates the strengths of both GAN-based…

  • Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings

    Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings arXiv:2511.06425v1 Announce Type: new Abstract: Interpretable representation learning is a central challenge in modern machine learning, particularly in high-dimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting their effectiveness in…

  • Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning

    Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning arXiv:2511.05050v1 Announce Type: new Abstract: In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional causal inference often focuses on unidirectional effects, overlooking the common bidirectional relationships in real-world phenomena.…

  • DoFlow: Causal Generative Flows for Interventional and Counterfactual Time-Series Prediction

    DoFlow: Causal Generative Flows for Interventional and Counterfactual Time-Series Prediction arXiv:2511.02137v1 Announce Type: new Abstract: Time-series forecasting increasingly demands not only accurate observational predictions but also causal forecasting under interventional and counterfactual queries in multivariate systems. We present DoFlow, a flow based generative model defined over a causal DAG that delivers coherent observational and interventional…

  • Gradient Boosted Mixed Models: Flexible Joint Estimation of Mean and Variance Components for Clustered Data

    Gradient Boosted Mixed Models: Flexible Joint Estimation of Mean and Variance Components for Clustered Data arXiv:2511.00217v1 Announce Type: new Abstract: Linear mixed models are widely used for clustered data, but their reliance on parametric forms limits flexibility in complex and high-dimensional settings. In contrast, gradient boosting methods achieve high predictive accuracy through nonparametric estimation, but…

  • Minimax-Optimal Two-Sample Test with Sliced Wasserstein

    Minimax-Optimal Two-Sample Test with Sliced Wasserstein arXiv:2510.27498v1 Announce Type: new Abstract: We study the problem of nonparametric two-sample testing using the sliced Wasserstein (SW) distance. While prior theoretical and empirical work indicates that the SW distance offers a promising balance between strong statistical guarantees and computational efficiency, its theoretical foundations for hypothesis testing remain limited.…

  • Bias-Corrected Data Synthesis for Imbalanced Learning

    Bias-Corrected Data Synthesis for Imbalanced Learning arXiv:2510.26046v1 Announce Type: new Abstract: Imbalanced data, where the positive samples represent only a small proportion compared to the negative samples, makes it challenging for classification problems to balance the false positive and false negative rates. A common approach to addressing the challenge involves generating synthetic data for the…

  • Beyond Normality: Reliable A/B Testing with Non-Gaussian Data

    Beyond Normality: Reliable A/B Testing with Non-Gaussian Data arXiv:2510.23666v1 Announce Type: new Abstract: A/B testing has become the cornerstone of decision-making in online markets, guiding how platforms launch new features, optimize pricing strategies, and improve user experience. In practice, we typically employ the pairwise $t$-test to compare outcomes between the treatment and control groups, thereby…

  • Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

    Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference arXiv:2510.21889v1 Announce Type: new Abstract: Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links, a recently developed method, assimilative causal inference (ACI), integrates observations with dynamical models. It utilizes Bayesian data assimilation…

  • Doubly-Regressing Approach for Subgroup Fairness

    Doubly-Regressing Approach for Subgroup Fairness arXiv:2510.21091v1 Announce Type: new Abstract: Algorithmic fairness is a socially crucial topic in real-world applications of AI. Among many notions of fairness, subgroup fairness is widely studied when multiple sensitive attributes (e.g., gender, race, age) are present. However, as the number of sensitive attributes grows, the number of subgroups increases…

  • Testing Most Influential Sets

    Testing Most Influential Sets arXiv:2510.20372v1 Announce Type: new Abstract: Small subsets of data with disproportionate influence on model outcomes can have dramatic impacts on conclusions, with a few data points sometimes overturning key findings. While recent work has developed methods to identify these emph{most influential sets}, no formal theory exists to determine when their influence…

  • Arbitrated Indirect Treatment Comparisons

    Arbitrated Indirect Treatment Comparisons arXiv:2510.18071v1 Announce Type: new Abstract: Matching-adjusted indirect comparison (MAIC) has been increasingly employed in health technology assessments (HTA). By reweighting subjects from a trial with individual participant data (IPD) to match the covariate summary statistics of another trial with only aggregate data (AgD), MAIC facilitates the estimation of a treatment effect…

  • Reliable data clustering with Bayesian community detection

    Reliable data clustering with Bayesian community detection arXiv:2510.15013v1 Announce Type: new Abstract: From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Yet widely used clustering methods, such as hierarchical clustering, k-means, and WGCNA, lack principled model selection, leaving them susceptible to noise. A common workaround…

  • Simplicial Gaussian Models: Representation and Inference

    Simplicial Gaussian Models: Representation and Inference arXiv:2510.12983v1 Announce Type: new Abstract: Probabilistic graphical models (PGMs) are powerful tools for representing statistical dependencies through graphs in high-dimensional systems. However, they are limited to pairwise interactions. In this work, we propose the simplicial Gaussian model (SGM), which extends Gaussian PGM to simplicial complexes. SGM jointly models random…

  • Kernel Treatment Effects with Adaptively Collected Data

    Kernel Treatment Effects with Adaptively Collected Data arXiv:2510.10245v1 Announce Type: new Abstract: Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d. assumptions that underpins classical asymptotics. At the same time, many questions of interest are distributional, extending beyond average effects. Kernel treatment effects (KTE) provide a…

  • Neural variational inference for cutting feedback during uncertainty propagation

    Neural variational inference for cutting feedback during uncertainty propagation arXiv:2510.10268v1 Announce Type: new Abstract: In many scientific applications, uncertainty of estimates from an earlier (upstream) analysis needs to be propagated in subsequent (downstream) Bayesian analysis, without feedback. Cutting feedback methods, also termed cut-Bayes, achieve this by constructing a cut-posterior distribution that prevents backward information flow.…

  • Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

    Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death arXiv:2510.07501v1 Announce Type: new Abstract: Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient,…

  • A Honest Cross-Validation Estimator for Prediction Performance

    A Honest Cross-Validation Estimator for Prediction Performance arXiv:2510.07649v1 Announce Type: new Abstract: Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the…

  • The analogy theorem in Hoare logic

    The analogy theorem in Hoare logic arXiv:2510.03685v1 Announce Type: new Abstract: The introduction of machine learning methods has led to significant advances in automation, optimization, and discoveries in various fields of science and technology. However, their widespread application faces a fundamental limitation: the transfer of models between data domains generally lacks a rigorous mathematical justification.…

  • CINDES: Classification induced neural density estimator and simulator

    CINDES: Classification induced neural density estimator and simulator arXiv:2510.00367v1 Announce Type: new Abstract: Neural network-based methods for (un)conditional density estimation have recently gained substantial attention, as various neural density estimators have outperformed classical approaches in real-data experiments. Despite these empirical successes, implementation can be challenging due to the need to ensure non-negativity and unit-mass constraints,…

  • On the Adversarial Robustness of Learning-based Conformal Novelty Detection

    On the Adversarial Robustness of Learning-based Conformal Novelty Detection arXiv:2510.00463v1 Announce Type: new Abstract: This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on AdaDetect, a powerful learning-based framework for novelty detection with finite-sample false discovery rate (FDR) control. While AdaDetect provides rigorous statistical guarantees under benign conditions, its behavior…

  • Conservative Decisions with Risk Scores

    Conservative Decisions with Risk Scores arXiv:2509.25588v1 Announce Type: new Abstract: In binary classification applications, conservative decision-making that allows for abstention can be advantageous. To this end, we introduce a novel approach that determines the optimal cutoff interval for risk scores, which can be directly available or derived from fitted models. Within this interval, the algorithm…

  • One-shot Conditional Sampling: MMD meets Nearest Neighbors

    One-shot Conditional Sampling: MMD meets Nearest Neighbors arXiv:2509.25507v1 Announce Type: new Abstract: How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference,…

  • Coupling Generative Modeling and an Autoencoder with the Causal Bridge

    Coupling Generative Modeling and an Autoencoder with the Causal Bridge arXiv:2509.25599v1 Announce Type: new Abstract: We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by assuming access to two separate…

  • SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions

    SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions arXiv:2509.21707v1 Announce Type: new Abstract: Real-world applications often face scarce labeled data due to the high cost and time requirements of gold-standard experiments, whereas unlabeled data are typically abundant. With the growing adoption of machine learning techniques, it has become increasingly feasible to generate multiple predicted…

  • Unsupervised Domain Adaptation with an Unobservable Source Subpopulation

    Unsupervised Domain Adaptation with an Unobservable Source Subpopulation arXiv:2509.20587v1 Announce Type: new Abstract: We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label $Y$ and a binary background (or environment) $A$. We focus on a challenging setting in which one such subpopulation in the source domain…

  • A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data

    A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data arXiv:2509.20636v1 Announce Type: new Abstract: The analysis of spatial data from biological imaging technology, such as imaging mass spectrometry (IMS) or imaging mass cytometry (IMC), is challenging because of a competitive sampling process which convolves signals from molecules in a single…

  • Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies

    Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies arXiv:2509.19707v1 Announce Type: new Abstract: Copulas are a fundamental tool for modelling multivariate dependencies in data, forming the method of choice in diverse fields and applications. However, the adoption of existing models for multimodal and high-dimensional dependencies is hindered by restrictive assumptions and poor scaling. In this…

  • DoubleGen: Debiased Generative Modeling of Counterfactuals

    DoubleGen: Debiased Generative Modeling of Counterfactuals arXiv:2509.16842v1 Announce Type: new Abstract: Generative models for counterfactual outcomes face two key sources of bias. Confounding bias arises when approaches fail to account for systematic differences between those who receive the intervention and those who do not. Misspecification bias arises when methods attempt to address confounding through estimation…

  • Interpretable Network-assisted Random Forest+

    Interpretable Network-assisted Random Forest+ arXiv:2509.15611v1 Announce Type: new Abstract: Machine learning algorithms often assume that training samples are independent. When data points are connected by a network, the induced dependency between samples is both a challenge, reducing effective sample size, and an opportunity to improve prediction by leveraging information from network neighbors. Multiple methods taking…

  • What is a good matching of probability measures? A counterfactual lens on transport maps

    What is a good matching of probability measures? A counterfactual lens on transport maps arXiv:2509.16027v1 Announce Type: new Abstract: Coupling probability measures lies at the core of many problems in statistics and machine learning, from domain adaptation to transfer learning and causal inference. Yet, even when restricted to deterministic transports, such couplings are not identifiable:…

  • Imputation-Powered Inference

    Imputation-Powered Inference arXiv:2509.13778v1 Announce Type: cross Abstract: Modern multi-modal and multi-site data frequently suffer from blockwise missingness, where subsets of features are missing for groups of individuals, creating complex patterns that challenge standard inference methods. Existing approaches have critical limitations: complete-case analysis discards informative data and is potentially biased; doubly robust estimators for non-monotone missingness-where…

  • Contrastive Network Representation Learning

    Contrastive Network Representation Learning arXiv:2509.11316v1 Announce Type: new Abstract: Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection. Motivated by challenges in brain connectivity data analysis that is characterized by subject-specific, high-dimensional,…

  • kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions

    kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions arXiv:2509.08366v1 Announce Type: new Abstract: We study a missing-value imputation method, termed kNNSampler, that imputes a given unit’s missing response by randomly sampling from the observed responses of the $k$ most similar units to the given unit in terms of the observed covariates. This method can sample…

  • Fast kernel methods: Sobolev, physics-informed, and additive models

    Fast kernel methods: Sobolev, physics-informed, and additive models arXiv:2509.02649v1 Announce Type: new Abstract: Kernel methods are powerful tools in statistical learning, but their cubic complexity in the sample size n limits their use on large-scale datasets. In this work, we introduce a scalable framework for kernel regression with O(n log n) complexity, fully leveraging GPU…

  • Probit Monotone BART

    Probit Monotone BART arXiv:2509.00263v1 Announce Type: new Abstract: Bayesian Additive Regression Trees (BART) of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction. Monotone BART (Chipman et al., 2022) is a recent development that allows BART to be more precise in estimating monotonic functions. We further these developments…

  • Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation

    Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation arXiv:2508.20942v1 Announce Type: new Abstract: In this paper, we extend the transfer learning classification framework from regression function-based methods to decision rules. We propose a novel methodology for modeling posterior drift through Bayes decision rules. By exploiting the geometric…

  • Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models

    Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models arXiv:2508.19249v1 Announce Type: cross Abstract: We present a new efficient hybrid parameter estimation method based on the idea, that if nonlinear dynamic models are stated in terms of a system of equations that is linear in terms of the parameters, then regularized ordinary least squares can…

  • Sparse minimum Redundancy Maximum Relevance for feature selection

    Sparse minimum Redundancy Maximum Relevance for feature selection arXiv:2508.18901v1 Announce Type: new Abstract: We propose a feature screening method that integrates both feature-feature and feature-target relationships. Inactive features are identified via a penalized minimum Redundancy Maximum Relevance (mRMR) procedure, which is the continuous version of the classic mRMR penalized by a non-convex regularizer, and where…

  • Comparing Model-agnostic Feature Selection Methods through Relative Efficiency

    Comparing Model-agnostic Feature Selection Methods through Relative Efficiency arXiv:2508.14268v1 Announce Type: new Abstract: Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest. Wrapper methods are commonly used because they are typically model-agnostic, even though they are computationally intensive. In this paper, we focus on feature selection methods related…

  • Structural Foundations for Leading Digit Laws: Beyond Probabilistic Mixtures

    Structural Foundations for Leading Digit Laws: Beyond Probabilistic Mixtures arXiv:2508.13237v1 Announce Type: new Abstract: This article presents a modern deterministic framework for the study of leading significant digit distributions in numerical data. Rather than relying on traditional probabilistic or mixture-based explanations, we demonstrate that the observed frequencies of leading digits are determined by the underlying…

  • An Introduction to Sliced Optimal Transport

    An Introduction to Sliced Optimal Transport arXiv:2508.12519v1 Announce Type: new Abstract: Sliced Optimal Transport (SOT) is a rapidly developing branch of optimal transport (OT) that exploits the tractability of one-dimensional OT problems. By combining tools from OT, integral geometry, and computational statistics, SOT enables fast and scalable computation of distances, barycenters, and kernels for probability…

  • On computing and the complexity of computing higher-order $U$-statistics, exactly

    On computing and the complexity of computing higher-order $U$-statistics, exactly arXiv:2508.12627v1 Announce Type: new Abstract: Higher-order $U$-statistics abound in fields such as statistics, machine learning, and computer science, but are known to be highly time-consuming to compute in practice. Despite their widespread appearance, a comprehensive study of their computational complexity is surprisingly lacking. This paper…

  • Counterfactual Survival Q Learning for Longitudinal Randomized Trials via Buckley James Boosting

    Counterfactual Survival Q Learning for Longitudinal Randomized Trials via Buckley James Boosting arXiv:2508.11060v1 Announce Type: new Abstract: We propose a Buckley James (BJ) Boost Q learning framework for estimating optimal dynamic treatment regimes under right censored survival data, tailored for longitudinal randomized clinical trial settings. The method integrates accelerated failure time models with iterative boosting…

  • Nonparametric learning of stochastic differential equations from sparse and noisy data

    Nonparametric learning of stochastic differential equations from sparse and noisy data arXiv:2508.11597v1 Announce Type: new Abstract: The paper proposes a systematic framework for building data-driven stochastic differential equation (SDE) models from sparse, noisy observations. Unlike traditional parametric approaches, which assume a known functional form for the drift, our goal here is to learn the entire…

  • Decorrelated feature importance from local sample weighting

    Decorrelated feature importance from local sample weighting arXiv:2508.06337v1 Announce Type: new Abstract: Feature importance (FI) statistics provide a prominent and valuable method of insight into the decision process of machine learning (ML) models, but their effectiveness has well-known limitations when correlation is present among the features in the training data. In this case, the FI…

  • Likelihood Matching for Diffusion Models

    Likelihood Matching for Diffusion Models arXiv:2508.03636v1 Announce Type: new Abstract: We propose a Likelihood Matching approach for training diffusion models by first establishing an equivalence between the likelihood of the target data distribution and a likelihood along the sample path of the reverse diffusion. To efficiently compute the reverse sample likelihood, a quasi-likelihood is considered…

  • Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design

    Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design arXiv:2508.01834v1 Announce Type: new Abstract: With advances in scientific computing, computer experiments are increasingly used for optimizing complex systems. However, for modern applications, e.g., the optimization of nuclear physics detectors, each experiment run can require hundreds of CPU hours, making…

  • AdapDISCOM: An Adaptive Sparse Regression Method for High-Dimensional Multimodal Data With Block-Wise Missingness and Measurement Errors

    AdapDISCOM: An Adaptive Sparse Regression Method for High-Dimensional Multimodal Data With Block-Wise Missingness and Measurement Errors arXiv:2508.00120v1 Announce Type: cross Abstract: Multimodal high-dimensional data are increasingly prevalent in biomedical research, yet they are often compromised by block-wise missingness and measurement errors, posing significant challenges for statistical inference and prediction. We propose AdapDISCOM, a novel adaptive…

  • Stacked SVD or SVD stacked? A Random Matrix Theory perspective on data integration

    Stacked SVD or SVD stacked? A Random Matrix Theory perspective on data integration arXiv:2507.22170v1 Announce Type: new Abstract: Modern data analysis increasingly requires identifying shared latent structure across multiple high-dimensional datasets. A commonly used model assumes that the data matrices are noisy observations of low-rank matrices with a shared singular subspace. In this case, two…

  • Perfect Clustering in Very Sparse Diverse Multiplex Networks

    Perfect Clustering in Very Sparse Diverse Multiplex Networks arXiv:2507.19423v1 Announce Type: new Abstract: The paper studies the DIverse MultiPLEx Signed Generalized Random Dot Product Graph (DIMPLE-SGRDPG) network model (Pensky (2024)), where all layers of the network have the same collection of nodes. In addition, all layers can be partitioned into groups such that the layers…

  • Sliding Window Informative Canonical Correlation Analysis

    Sliding Window Informative Canonical Correlation Analysis arXiv:2507.17921v1 Announce Type: new Abstract: Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a novel extension of CCA to the online, streaming data setting: Sliding Window Informative Canonical Correlation Analysis (SWICCA). Our method uses a streaming…

  • Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis

    Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis arXiv:2507.16682v1 Announce Type: new Abstract: Regularized linear discriminant analysis (RLDA) is a widely used tool for classification and dimensionality reduction, but its performance in high-dimensional scenarios is inconsistent. Existing theoretical analyses of RLDA often lack clear insight into how data structure affects classification performance.…

  • Conformalized Regression for Continuous Bounded Outcomes

    Conformalized Regression for Continuous Bounded Outcomes arXiv:2507.14023v1 Announce Type: new Abstract: Regression problems with bounded continuous outcomes frequently arise in real-world statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting a response associated with a new covariate value. Most of the existing statistical…

  • Newfluence: Boosting Model interpretability and Understanding in High Dimensions

    Newfluence: Boosting Model interpretability and Understanding in High Dimensions arXiv:2507.11895v1 Announce Type: new Abstract: The increasing complexity of machine learning (ML) and artificial intelligence (AI) models has created a pressing need for tools that help scientists, engineers, and policymakers interpret and refine model decisions and predictions. Influence functions, originating from robust statistics, have emerged as…