mailitics

Category: stat.ME

  • Physics-informed machine learning: A mathematical framework with applications to time series forecasting

    Physics-informed machine learning: A mathematical framework with applications to time series forecasting arXiv:2507.08906v1 Announce Type: new Abstract: Physics-informed machine learning (PIML) is an emerging framework that integrates physical knowledge into machine learning models. This physical prior often takes the form of a partial differential equation (PDE) system that the regression function must satisfy. In the…

  • CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk

    CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk arXiv:2507.08150v1 Announce Type: new Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling, especially in regression tasks. Existing methods typically address either aleatoric uncertainty from measurement noise or epistemic uncertainty from limited data, but not necessarily both in a balanced way. We propose CLEAR, a calibration…

  • Class conditional conformal prediction for multiple inputs by p-value aggregation

    Class conditional conformal prediction for multiple inputs by p-value aggregation arXiv:2507.07150v1 Announce Type: new Abstract: Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. This work introduces an innovative refinement to these methods for classification tasks, specifically tailored for scenarios where multiple observations (multi-inputs) of a…

  • Fast Gaussian Processes under Monotonicity Constraints

    Fast Gaussian Processes under Monotonicity Constraints arXiv:2507.06677v1 Announce Type: new Abstract: Gaussian processes (GPs) are widely used as surrogate models for complicated functions in scientific and engineering applications. In many cases, prior knowledge about the function to be approximated, such as monotonicity, is available and can be leveraged to improve model fidelity. Incorporating such constraints…

  • Conformal Prediction for Long-Tailed Classification

    Conformal Prediction for Long-Tailed Classification arXiv:2507.06867v1 Announce Type: new Abstract: Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii)…

  • Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data

    Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data arXiv:2507.03681v1 Announce Type: new Abstract: Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover effect heterogeneity over patient characteristics, limiting their value for personalized decision-making. To address this, we propose the QR-learner, a model-agnostic…

  • Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression

    Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression arXiv:2507.03736v1 Announce Type: new Abstract: In this work, we propose a novel methodology for robustly estimating particle size distributions from optical scattering measurements using constrained Gaussian process regression. The estimation of particle size distributions is commonly formulated as a Fredholm integral equation of…

  • It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation

    It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation arXiv:2507.02275v1 Announce Type: new Abstract: Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confounders on treatment and outcomes). Here, we find that the answer depends in a…

  • Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles

    Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles arXiv:2507.01542v1 Announce Type: new Abstract: Gaussian mixture models (GMMs) are ubiquitous in statistical learning, particularly for unsupervised problems. While full GMMs suffer from the overparameterization of their covariance matrices in high-dimensional spaces, spherical GMMs (with isotropic covariance matrices) certainly lack flexibility to fit certain anisotropic distributions. Connecting…

  • Disentangled Feature Importance

    Disentangled Feature Importance arXiv:2507.00260v1 Announce Type: new Abstract: Feature importance quantification faces a fundamental challenge: when predictors are correlated, standard methods systematically underestimate their contributions. We prove that major existing approaches target identical population functionals under squared-error loss, revealing why they share this correlation-induced bias. To address this limitation, we introduce emph{Disentangled Feature Importance (DFI)},…

  • GRAND: Graph Release with Assured Node Differential Privacy

    GRAND: Graph Release with Assured Node Differential Privacy arXiv:2507.00402v1 Announce Type: new Abstract: Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data — particularly at the node level — remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based…

  • Valid Selection among Conformal Sets

    Valid Selection among Conformal Sets arXiv:2506.20173v1 Announce Type: new Abstract: Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be available, arising from different models or methodologies. However, selecting the most desirable set, such as the smallest, can invalidate the coverage guarantees. To…

  • POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes

    POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes arXiv:2506.20406v1 Announce Type: new Abstract: Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual trajectories, such as healthcare, education, and digital interventions. However, existing statistical methods often rely on…

  • Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions

    Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions arXiv:2506.19010v1 Announce Type: new Abstract: Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the…

  • Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond

    Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond arXiv:2506.14950v1 Announce Type: new Abstract: Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest that satisfies some conditional moment equalities. Specifically, many techniques…

  • Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation

    Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation arXiv:2506.12183v1 Announce Type: new Abstract: 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…

  • A Transfer Learning Framework for Multilayer Networks via Model Averaging

    A Transfer Learning Framework for Multilayer Networks via Model Averaging arXiv:2506.12455v1 Announce Type: new Abstract: 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…

  • LLM-Powered CPI Prediction Inference with Online Text Time Series

    LLM-Powered CPI Prediction Inference with Online Text Time Series arXiv:2506.09516v1 Announce Type: new Abstract: Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LLMs), there is growing potential to leverage high-frequency online text…

  • Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces

    Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces arXiv:2506.08325v1 Announce Type: new Abstract: Depth measures are powerful tools for defining level sets in emerging, non–standard, and complex random objects such as high-dimensional multivariate data, functional data, and random graphs. Despite their favorable theoretical properties, the integration of…

  • Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series

    Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series arXiv:2506.05354v1 Announce Type: cross Abstract: Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving…

  • On the Wasserstein Geodesic Principal Component Analysis of probability measures

    On the Wasserstein Geodesic Principal Component Analysis of probability measures arXiv:2506.04480v1 Announce Type: new Abstract: This paper focuses on Geodesic Principal Component Analysis (GPCA) on a collection of probability distributions using the Otto-Wasserstein geometry. The goal is to identify geodesic curves in the space of probability measures that best capture the modes of variation of…

  • Nonlinear Causal Discovery for Grouped Data

    Nonlinear Causal Discovery for Grouped Data arXiv:2506.05120v1 Announce Type: new Abstract: Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather…

  • Assumption-free stability for ranking problems

    Assumption-free stability for ranking problems arXiv:2506.02257v1 Announce Type: new Abstract: In this work, we consider ranking problems among a finite set of candidates: for instance, selecting the top-$k$ items among a larger list of candidates or obtaining the full ranking of all items in the set. These problems are often unstable, in the sense that…

  • Bayesian Data Sketching for Varying Coefficient Regression Models

    Bayesian Data Sketching for Varying Coefficient Regression Models arXiv:2506.00270v1 Announce Type: new Abstract: Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large data applications, primarily due to prohibitively slow posterior computations using Markov chain Monte Carlo (MCMC) algorithms. We introduce Bayesian…

  • STACI: Spatio-Temporal Aleatoric Conformal Inference

    STACI: Spatio-Temporal Aleatoric Conformal Inference arXiv:2505.21658v1 Announce Type: new Abstract: Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while scalable, typically assume a simplistic independent covariance matrix for the response, failing to capture the underlying correlation structure. However, spatio-temporal GPs suffer from issues of scalability…

  • Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models

    Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models arXiv:2505.20536v1 Announce Type: new Abstract: This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel data models, that employs flexible model structures and powerful…

  • Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schr”odinger Bridge’s End

    Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schr”odinger Bridge’s End arXiv:2505.16082v1 Announce Type: new Abstract: Scientists often want to make predictions beyond the observed time horizon of “snapshot” data following latent stochastic dynamics. For example, in time course single-cell mRNA profiling, scientists have access to cellular transcriptional state measurements (snapshots) from different biological replicates at…

  • Fairness-aware Bayes optimal functional classification

    Fairness-aware Bayes optimal functional classification arXiv:2505.09471v1 Announce Type: new Abstract: Algorithmic fairness has become a central topic in machine learning, and mitigating disparities across different subpopulations has emerged as a rapidly growing research area. In this paper, we systematically study the classification of functional data under fairness constraints, ensuring the disparity level of the classifier…

  • GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds

    GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds arXiv:2505.02972v1 Announce Type: new Abstract: Multi-Task Learning (MTL) seeks to boost statistical power and learning efficiency by discovering structure shared across related tasks. State-of-the-art MTL representation methods, however, usually treat the latent representation matrix as a point in ordinary Euclidean space, ignoring its often non-Euclidean geometry, thus…

  • Reinforcement Learning with Continuous Actions Under Unmeasured Confounding

    Reinforcement Learning with Continuous Actions Under Unmeasured Confounding arXiv:2505.00304v1 Announce Type: new Abstract: This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we…

  • Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction

    Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction arXiv:2505.00310v1 Announce Type: new Abstract: Robust estimation of heterogeneous treatment effects is a fundamental challenge for optimal decision-making in domains ranging from personalized medicine to educational policy. In recent years, predictive machine learning has emerged as a valuable toolbox for causal estimation, enabling more flexible…

  • ReLU integral probability metric and its applications

    ReLU integral probability metric and its applications arXiv:2504.18897v1 Announce Type: new Abstract: We propose a parametric integral probability metric (IPM) to measure the discrepancy between two probability measures. The proposed IPM leverages a specific parametric family of discriminators, such as single-node neural networks with ReLU activation, to effectively distinguish between distributions, making it applicable in…

  • Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests

    Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests arXiv:2504.18498v1 Announce Type: new Abstract: Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This…

  • Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees

    Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees arXiv:2504.16356v1 Announce Type: new Abstract: Graphical models are widely used in diverse application domains to model the conditional dependencies amongst a collection of random variables. In this paper, we consider settings where the graph structure is covariate-dependent, and investigate a deep neural network-based approach to…

  • Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry

    Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry arXiv:2504.14164v1 Announce Type: new Abstract: We introduce a novel, geometry-aware distance metric for the family of von Mises-Fisher (vMF) distributions, which are fundamental models for directional data on the unit hypersphere. Although the vMF distribution is widely employed in a variety of probabilistic learning tasks involving…

  • Robust and Scalable Variational Bayes

    Robust and Scalable Variational Bayes arXiv:2504.12528v1 Announce Type: new Abstract: We propose a robust and scalable framework for variational Bayes (VB) that effectively handles outliers and contamination of arbitrary nature in large datasets. Our approach divides the dataset into disjoint subsets, computes the posterior for each subset, and applies VB approximation independently to these posteriors.…

  • Towards Interpretable Deep Generative Models via Causal Representation Learning

    Towards Interpretable Deep Generative Models via Causal Representation Learning arXiv:2504.11609v1 Announce Type: new Abstract: Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods’ surprising performance is due in part to their ability to learn implicit “representations”…

  • A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior

    A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior arXiv:2504.06372v1 Announce Type: cross Abstract: Inference and estimation are fundamental aspects of statistics, system identification and machine learning. For most inference problems, prior knowledge is available on the system to be modeled, and Bayesian analysis is a natural framework to impose such prior information in the form…

  • Survey on Algorithms for multi-index models

    Survey on Algorithms for multi-index models arXiv:2504.05426v1 Announce Type: new Abstract: We review the literature on algorithms for estimating the index space in a multi-index model. The primary focus is on computationally efficient (polynomial-time) algorithms in Gaussian space, the assumptions under which consistency is guaranteed by these methods, and their sample complexity. In many cases,…

  • High-dimensional ridge regression with random features for non-identically distributed data with a variance profile

    High-dimensional ridge regression with random features for non-identically distributed data with a variance profile arXiv:2504.03035v1 Announce Type: new Abstract: The behavior of the random feature model in the high-dimensional regression framework has become a popular issue of interest in the machine learning literature}. This model is generally considered for feature vectors $x_i = Sigma^{1/2} x_i’$,…

  • Density estimation via mixture discrepancy and moments

    Density estimation via mixture discrepancy and moments arXiv:2504.01570v1 Announce Type: new Abstract: With the aim of generalizing histogram statistics to higher dimensional cases, density estimation via discrepancy based sequential partition (DSP) has been proposed [D. Li, K. Yang, W. Wong, Advances in Neural Information Processing Systems (2016) 1099-1107] to learn an adaptive piecewise constant approximation…

  • Rolled Gaussian process models for curves on manifolds

    Rolled Gaussian process models for curves on manifolds arXiv:2503.21980v1 Announce Type: cross Abstract: Given a planar curve, imagine rolling a sphere along that curve without slipping or twisting, and by this means tracing out a curve on the sphere. It is well known that such a rolling operation induces a local isometry between the sphere…

  • Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding

    Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding arXiv:2503.20546v1 Announce Type: new Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables. In settings without selection bias or confounding, $E[Y|do(X)] =…

  • Minimum Volume Conformal Sets for Multivariate Regression

    Minimum Volume Conformal Sets for Multivariate Regression arXiv:2503.19068v1 Announce Type: new Abstract: Conformal prediction provides a principled framework for constructing predictive sets with finite-sample validity. While much of the focus has been on univariate response variables, existing multivariate methods either impose rigid geometric assumptions or rely on flexible but computationally expensive approaches that do not…

  • Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates

    Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates arXiv:2503.16941v1 Announce Type: new Abstract: Personalized services are central to today’s digital landscape, where online decision-making is commonly formulated as contextual bandit problems. Two key challenges emerge in modern applications: high-dimensional covariates and the need for nonparametric models to capture complex reward-covariate…

  • The Hardness of Validating Observational Studies with Experimental Data

    The Hardness of Validating Observational Studies with Experimental Data arXiv:2503.14795v1 Announce Type: new Abstract: Observational data is often readily available in large quantities, but can lead to biased causal effect estimates due to the presence of unobserved confounding. Recent works attempt to remove this bias by supplementing observational data with experimental data, which, when available,…

  • Nonlinear Principal Component Analysis with Random Bernoulli Features for Process Monitoring

    Nonlinear Principal Component Analysis with Random Bernoulli Features for Process Monitoring arXiv:2503.12456v1 Announce Type: new Abstract: The process generates substantial amounts of data with highly complex structures, leading to the development of numerous nonlinear statistical methods. However, most of these methods rely on computations involving large-scale dense kernel matrices. This dependence poses significant challenges in…

  • Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks

    Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks arXiv:2503.10496v1 Announce Type: new Abstract: Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions. However, ANNs often suffer from over-parameterization, complicating interpretation and raising uncertainty issues. Bayesian neural networks (BNNs) address the latter by representing weights as probability distributions, allowing…

  • Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data

    Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data arXiv:2503.09097v1 Announce Type: new Abstract: In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep learning methods for analyzing censored time-to-event data, especially when dealing with high-dimensional predictors that are complexly interrelated. Many…

  • Bayesian Optimization for Robust Identification of Ornstein-Uhlenbeck Model

    Bayesian Optimization for Robust Identification of Ornstein-Uhlenbeck Model arXiv:2503.06381v1 Announce Type: new Abstract: This paper deals with the identification of the stochastic Ornstein-Uhlenbeck (OU) process error model, which is characterized by an inverse time constant, and the unknown variances of the process and observation noises. Although the availability of the explicit expression of the log-likelihood…

  • Multiple Linked Tensor Factorization

    Multiple Linked Tensor Factorization arXiv:2502.20286v1 Announce Type: new Abstract: In biomedical research and other fields, it is now common to generate high content data that are both multi-source and multi-way. Multi-source data are collected from different high-throughput technologies while multi-way data are collected over multiple dimensions, yielding multiple tensor arrays. Integrative analysis of these data…

  • Near-Optimal Approximations for Bayesian Inference in Function Space

    Near-Optimal Approximations for Bayesian Inference in Function Space arXiv:2502.18279v1 Announce Type: new Abstract: We propose a scalable inference algorithm for Bayes posteriors defined on a reproducing kernel Hilbert space (RKHS). Given a likelihood function and a Gaussian random element representing the prior, the corresponding Bayes posterior measure $Pi_{text{B}}$ can be obtained as the stationary distribution…

  • Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness

    Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness arXiv:2502.16391v1 Announce Type: new Abstract: In this paper, we explore the theoretical properties of subspace recovery using Winsorized Principal Component Analysis (WPCA), utilizing a common data transformation technique that caps extreme values to mitigate the impact of outliers. Despite the widespread use of winsorization in…

  • Conformal Prediction under L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations

    Conformal Prediction under L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations arXiv:2502.14105v1 Announce Type: new Abstract: Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a significant challenge. This paper addresses this limitation by modeling distribution shifts using L’evy-Prokhorov (LP) ambiguity sets, which…

  • Prediction-Powered Adaptive Shrinkage Estimation

    Prediction-Powered Adaptive Shrinkage Estimation arXiv:2502.14166v1 Announce Type: new Abstract: Prediction-Powered Inference (PPI) is a powerful framework for enhancing statistical estimates by combining limited gold-standard data with machine learning (ML) predictions. While prior work has demonstrated PPI’s benefits for individual statistical tasks, modern applications require answering numerous parallel statistical questions. We introduce Prediction-Powered Adaptive Shrinkage (PAS),…

  • An Efficient Permutation-Based Kernel Two-Sample Test

    An Efficient Permutation-Based Kernel Two-Sample Test arXiv:2502.13570v1 Announce Type: new Abstract: Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics and machine learning with broad scientific applications. In the context of nonparametric testing, maximum mean discrepancy (MMD) has gained popularity as a test statistic due…

  • Green LIME: Improving AI Explainability through Design of Experiments

    Green LIME: Improving AI Explainability through Design of Experiments arXiv:2502.12753v1 Announce Type: new Abstract: In artificial intelligence (AI), the complexity of many models and processes often surpasses human interpretability, making it challenging to understand why a specific prediction is made. This lack of transparency is particularly problematic in critical fields like healthcare, where trust in…

  • Federated Variational Inference for Bayesian Mixture Models

    Federated Variational Inference for Bayesian Mixture Models arXiv:2502.12684v1 Announce Type: new Abstract: We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled ‘divide and conquer’ inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, followed by…

  • Forecasting time series with constraints

    Forecasting time series with constraints arXiv:2502.10485v1 Announce Type: new Abstract: Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for…

  • Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm

    Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm arXiv:2502.10650v1 Announce Type: new Abstract: Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accurate parameter estimation. Variational Autoencoders (VAEs) have been one of the most…

  • Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction

    Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction arXiv:2502.05676v1 Announce Type: new Abstract: Ensuring model calibration is critical for reliable predictions, yet popular distribution-free methods, such as histogram binning and isotonic regression, provide only asymptotic guarantees. We introduce a unified framework for Venn and Venn-Abers calibration, generalizing Vovk’s binary classification approach to arbitrary…

  • Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect

    Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect arXiv:2502.04673v1 Announce Type: new Abstract: Estimation and inference for the Average Treatment Effect (ATE) is a cornerstone of causal inference and often serves as the foundation for developing procedures for more complicated settings. Although traditionally analyzed in a batch setting, recent advances in martingale theory…

  • Decentralized Inference for Distributed Geospatial Data Using Low-Rank Models

    Decentralized Inference for Distributed Geospatial Data Using Low-Rank Models arXiv:2502.00309v1 Announce Type: new Abstract: Advancements in information technology have enabled the creation of massive spatial datasets, driving the need for scalable and efficient computational methodologies. While offering viable solutions, centralized frameworks are limited by vulnerabilities such as single-point failures and communication bottlenecks. This paper presents…

  • LITE: Efficiently Estimating Gaussian Probability of Maximality

    LITE: Efficiently Estimating Gaussian Probability of Maximality arXiv:2501.13535v1 Announce Type: new Abstract: We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challenge in applications ranging from Bayesian optimization to reinforcement learning, where the PoM not…

  • SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules

    SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules arXiv:2501.09900v1 Announce Type: new Abstract: Bayesian Additive Regression Trees [BART, Chipman et al., 2010] have gained significant popularity due to their remarkable predictive performance and ability to quantify uncertainty. However, standard decision tree models rely on recursive data splits at each decision node, using…

  • Causal vs. Anticausal merging of predictors

    Causal vs. Anticausal merging of predictors arXiv:2501.08426v1 Announce Type: cross Abstract: We study the differences arising from merging predictors in the causal and anticausal directions using the same data. In particular we study the asymmetries that arise in a simple model where we merge the predictors using one binary variable as target and two continuous…

  • Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing

    Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing arXiv:2501.06366v1 Announce Type: new Abstract: When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy may disproportionately allocate efficacious actions to one subpopulation, creating or exacerbating disparities in other socioeconomically-disadvantaged subgroups. These biases…

  • Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age

    Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age arXiv:2501.06868v1 Announce Type: new Abstract: Many problems within personalized medicine and digital health rely on the analysis of continuous-time functional biomarkers and other complex data structures emerging from high-resolution patient monitoring. In this context, this work proposes new optimization-based variable selection…

  • Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference

    Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference arXiv:2501.06926v1 Announce Type: new Abstract: Double reinforcement learning (DRL) enables statistically efficient inference on the value of a policy in a nonparametric Markov Decision Process (MDP) given trajectories generated by another policy. However, this approach necessarily requires stringent overlap between…

  • RieszBoost: Gradient Boosting for Riesz Regression

    RieszBoost: Gradient Boosting for Riesz Regression arXiv:2501.04871v1 Announce Type: new Abstract: Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified treatment policy. By the Riesz representation theorem, these functionals can be expressed as the expected product of the conditional expectation…

  • Class-Balance Bias in Regularized Regression

    Class-Balance Bias in Regularized Regression arXiv:2501.03821v1 Announce Type: new Abstract: Regularized models are often sensitive to the scales of the features in the data and it has therefore become standard practice to normalize (center and scale) the features before fitting the model. But there are many different ways to normalize the features and the choice…

  • Post Launch Evaluation of Policies in a High-Dimensional Setting

    Post Launch Evaluation of Policies in a High-Dimensional Setting arXiv:2501.00119v1 Announce Type: new Abstract: A/B tests, also known as randomized controlled experiments (RCTs), are the gold standard for evaluating the impact of new policies, products, or decisions. However, these tests can be costly in terms of time and resources, potentially exposing users, customers, or other…

  • Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models

    Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models arXiv:2412.20586v1 Announce Type: new Abstract: Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In this study, we test and improve the robustness of parameter estimation using amortized Bayesian inference (ABI)…

  • Deep learning joint extremes of metocean variables using the SPAR model

    Deep learning joint extremes of metocean variables using the SPAR model arXiv:2412.15808v1 Announce Type: new Abstract: This paper presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When considered in polar coordinates, the problem of modelling multivariate extremes is transformed to one of…

  • Adaptive Nonparametric Perturbations of Parametric Bayesian Models

    Adaptive Nonparametric Perturbations of Parametric Bayesian Models arXiv:2412.10683v2 Announce Type: cross Abstract: Parametric Bayesian modeling offers a powerful and flexible toolbox for scientific data analysis. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In this paper we study nonparametrically perturbed parametric (NPP) Bayesian models, in which a parametric…

  • Matrix Completion via Residual Spectral Matching

    Matrix Completion via Residual Spectral Matching arXiv:2412.10005v1 Announce Type: new Abstract: Noisy matrix completion has attracted significant attention due to its applications in recommendation systems, signal processing and image restoration. Most existing works rely on (weighted) least squares methods under various low-rank constraints. However, minimizing the sum of squared residuals is not always efficient, as…

  • Spectral Differential Network Analysis for High-Dimensional Time Series

    Spectral Differential Network Analysis for High-Dimensional Time Series arXiv:2412.07905v1 Announce Type: cross Abstract: Spectral networks derived from multivariate time series data arise in many domains, from brain science to Earth science. Often, it is of interest to study how these networks change under different conditions. For instance, to better understand epilepsy, it would be interesting…

  • Ranking of Large Language Model with Nonparametric Prompts

    Ranking of Large Language Model with Nonparametric Prompts arXiv:2412.05506v1 Announce Type: new Abstract: We consider the inference for the ranking of large language models (LLMs). Alignment arises as a big challenge to mitigate hallucinations in the use of LLMs. Ranking LLMs has been shown as a well-performing tool to improve alignment based on the best-of-$N$…

  • Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution

    Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution arXiv:2412.05759v1 Announce Type: new Abstract: Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to…

  • Modeling High-Dimensional Dependent Data in the Presence of Many Explanatory Variables and Weak Signals

    Modeling High-Dimensional Dependent Data in the Presence of Many Explanatory Variables and Weak Signals arXiv:2412.04736v1 Announce Type: cross Abstract: This article considers a novel and widely applicable approach to modeling high-dimensional dependent data when a large number of explanatory variables are available and the signal-to-noise ratio is low. We postulate that a $p$-dimensional response series…

  • A Flexible Defense Against the Winner’s Curse

    A Flexible Defense Against the Winner’s Curse arXiv:2411.18569v1 Announce Type: new Abstract: Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly,…

  • Isometry pursuit

    Isometry pursuit arXiv:2411.18502v1 Announce Type: new Abstract: Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices. It consists of a novel normalization method followed by multitask basis pursuit. Applied to Jacobians of putative coordinate functions, it helps identity isometric embeddings from within interpretable dictionaries. We provide theoretical and experimental results justifying…

  • When Is Heterogeneity Actionable for Personalization?

    When Is Heterogeneity Actionable for Personalization? arXiv:2411.16552v1 Announce Type: cross Abstract: Targeting and personalization policies can be used to improve outcomes beyond the uniform policy that assigns the best performing treatment in an A/B test to everyone. Personalization relies on the presence of heterogeneity of treatment effects, yet, as we show in this paper, heterogeneity…