mailitics

Category: stat.AP

  • Bayesian Modeling of Collatz Stopping Times: A Probabilistic Machine Learning Perspective

    Bayesian Modeling of Collatz Stopping Times: A Probabilistic Machine Learning Perspective arXiv:2603.04479v1 Announce Type: new Abstract: We study the Collatz total stopping time $tau(n)$ over $nle 10^7$ from a probabilistic machine learning viewpoint. Empirically, $tau(n)$ is a skewed and heavily overdispersed count with pronounced arithmetic heterogeneity. We develop two complementary models. First, a Bayesian hierarchical…

  • 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…

  • Estimation of instrument and noise parameters for inverse problem based on prior diffusion model

    Estimation of instrument and noise parameters for inverse problem based on prior diffusion model arXiv:2602.11711v1 Announce Type: new Abstract: This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where regularization is introduced in a Bayesian framework and the prior is modeled by a…

  • Singular Bayesian Neural Networks

    Singular Bayesian Neural Networks arXiv:2602.00387v1 Announce Type: new Abstract: Bayesian neural networks promise calibrated uncertainty but require $O(mn)$ parameters for standard mean-field Gaussian posteriors. We argue this cost is often unnecessary, particularly when weight matrices exhibit fast singular value decay. By parameterizing weights as $W = AB^{top}$ with $A in mathbb{R}^{m times r}$, $B in…

  • It’s all the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms

    It’s all the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms arXiv:2601.22378v1 Announce Type: new Abstract: Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching algorithms and applications in machine learning. We prove that under certain conditions in an…

  • Efficient Causal Structure Learning via Modular Subgraph Integration

    Efficient Causal Structure Learning via Modular Subgraph Integration arXiv:2601.21014v1 Announce Type: new Abstract: Learning causal structures from observational data remains a fundamental yet computationally intensive task, particularly in high-dimensional settings where existing methods face challenges such as the super-exponential growth of the search space and increasing computational demands. To address this, we introduce VISTA (Voting-based…

  • 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,…

  • Long-Term Probabilistic Forecast of Vegetation Conditions Using Climate Attributes in the Four Corners Region

    Long-Term Probabilistic Forecast of Vegetation Conditions Using Climate Attributes in the Four Corners Region arXiv:2601.16347v1 Announce Type: cross Abstract: Weather conditions can drastically alter the state of crops and rangelands, and in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables…

  • Multi-task Modeling for Engineering Applications with Sparse Data

    Multi-task Modeling for Engineering Applications with Sparse Data arXiv:2601.05910v1 Announce Type: new Abstract: Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while low-fidelity data is more abundant. This paper introduces an Multi-Task Gaussian Processes (MTGP) framework tailored for engineering systems characterized…

  • Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem

    Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem arXiv:2601.06009v1 Announce Type: new Abstract: We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our approach is based on classical excursion and crossing theorems for continuous semimartingales, which correlates number $N_varepsilon$ of excursions of magnitude…

  • 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…

  • Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Functional Data Classification

    Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Functional Data Classification arXiv:2512.07888v1 Announce Type: new Abstract: Classification of functional data where observations are curves or trajectories poses unique challenges, particularly under severe class imbalance. Traditional Random Forest algorithms, while robust for tabular data, often fail to capture the intrinsic structure of functional observations and…

  • 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…

  • Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands

    Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands arXiv:2511.13911v1 Announce Type: new Abstract: Despite recent progress in predicting biomarker trajectories from real clinical data, uncertainty in the predictions poses high-stakes risks (e.g., misdiagnosis) that limit their clinical deployment. To enable safe and reliable use of such predictions in healthcare, we introduce a conformal method…

  • Bayesian Evaluation of Large Language Model Behavior

    Bayesian Evaluation of Large Language Model Behavior arXiv:2511.10661v1 Announce Type: cross Abstract: It is increasingly important to evaluate how text generation systems based on large language models (LLMs) behave, such as their tendency to produce harmful output or their sensitivity to adversarial inputs. Such evaluations often rely on a curated benchmark set of input prompts…

  • Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling

    Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling arXiv:2511.09722v1 Announce Type: new Abstract: Minerals play a critical role in the advanced energy technologies necessary for decarbonization, but characterizing mineral deposits hidden underground remains costly and challenging. Inspired by recent progress in generative modeling, we develop a learning method which infers the locations of minerals…

  • Bifidelity Karhunen-Lo`eve Expansion Surrogate with Active Learning for Random Fields

    Bifidelity Karhunen-Lo`eve Expansion Surrogate with Active Learning for Random Fields arXiv:2511.03756v1 Announce Type: new Abstract: We present a bifidelity Karhunen-Lo`eve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs. The approach combines the spectral efficiency of the KLE with polynomial chaos expansions (PCEs) to preserve an explicit mapping between input uncertainties…

  • Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models

    Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models arXiv:2510.19999v1 Announce Type: new Abstract: We present a novel enhanced cyclic coordinate descent (ECCD) framework for solving generalized linear models with elastic net constraints that reduces training time in comparison to existing state-of-the-art methods. We redesign the CD method by performing a Taylor expansion…

  • Signature Kernel Scoring Rule as Spatio-Temporal Diagnostic for Probabilistic Forecasting

    Signature Kernel Scoring Rule as Spatio-Temporal Diagnostic for Probabilistic Forecasting arXiv:2510.19110v1 Announce Type: new Abstract: Modern weather forecasting has increasingly transitioned from numerical weather prediction (NWP) to data-driven machine learning forecasting techniques. While these new models produce probabilistic forecasts to quantify uncertainty, their training and evaluation may remain hindered by conventional scoring rules, primarily MSE,…

  • Topology of Currencies: Persistent Homology for FX Co-movements: A Comparative Clustering Study

    Topology of Currencies: Persistent Homology for FX Co-movements: A Comparative Clustering Study arXiv:2510.19306v1 Announce Type: new Abstract: This study investigates whether Topological Data Analysis (TDA) can provide additional insights beyond traditional statistical methods in clustering currency behaviours. We focus on the foreign exchange (FX) market, which is a complex system often exhibiting non-linear and high-dimensional…

  • 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…

  • Bayesian Nonparametric Dynamical Clustering of Time Series

    Bayesian Nonparametric Dynamical Clustering of Time Series arXiv:2510.06919v1 Announce Type: new Abstract: We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the…

  • Domain-Shift-Aware Conformal Prediction for Large Language Models

    Domain-Shift-Aware Conformal Prediction for Large Language Models arXiv:2510.05566v1 Announce Type: new Abstract: Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under…

  • Scalable extensions to given-data Sobol’ index estimators

    Scalable extensions to given-data Sobol’ index estimators arXiv:2509.09078v1 Announce Type: new Abstract: Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol’ index computation for computationally expensive models and models with many inputs. However, the limitations of existing methods still preclude their application to models with an extremely large number of inputs.…

  • Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation

    Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation arXiv:2509.08163v1 Announce Type: cross Abstract: Ensuring equitable treatment (fairness) across protected attributes (such as gender or ethnicity) is a critical issue in machine learning. Most existing literature focuses on binary classification, but achieving fairness in regression tasks-such as insurance pricing or hiring score assessments-is equally…

  • Assessing One-Dimensional Cluster Stability by Extreme-Point Trimming

    Assessing One-Dimensional Cluster Stability by Extreme-Point Trimming arXiv:2509.00258v1 Announce Type: new Abstract: We develop a probabilistic method for assessing the tail behavior and geometric stability of one-dimensional n i.i.d. samples by tracking how their span contracts when the most extreme points are trimmed. Central to our approach is the diameter-shrinkage ratio, that quantifies the relative…

  • Tree-like Pairwise Interaction Networks

    Tree-like Pairwise Interaction Networks arXiv:2508.15678v1 Announce Type: new Abstract: Modeling feature interactions in tabular data remains a key challenge in predictive modeling, for example, as used for insurance pricing. This paper proposes the Tree-like Pairwise Interaction Network (PIN), a novel neural network architecture that explicitly captures pairwise feature interactions through a shared feed-forward neural network…

  • Can synthetic data reproduce real-world findings in epidemiology? A replication study using tree-based generative AI

    Can synthetic data reproduce real-world findings in epidemiology? A replication study using tree-based generative AI arXiv:2508.14936v1 Announce Type: cross Abstract: Generative artificial intelligence for synthetic data generation holds substantial potential to address practical challenges in epidemiology. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies…

  • The C-index Multiverse

    The C-index Multiverse arXiv:2508.14821v1 Announce Type: new Abstract: Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between…

  • 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…

  • Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting

    Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting arXiv:2508.00040v1 Announce Type: cross Abstract: This work integrates Bayesian regime detection with conditional neural processes for 24-hour electricity price prediction in the German market. Our methodology integrates regime detection using a disentangled sticky hierarchical Dirichlet process hidden Markov model (DS-HDP-HMM) applied to…

  • Predicting Parkinson’s Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers

    Predicting Parkinson’s Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers arXiv:2507.20058v1 Announce Type: new Abstract: Predicting Parkinson’s Disease (PD) progression is crucial, and voice biomarkers offer a non-invasive method for tracking symptom severity (UPDRS scores) through telemonitoring. Analyzing this longitudinal data is challenging due to within-subject correlations and…

  • Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification

    Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification arXiv:2507.18824v1 Announce Type: cross Abstract: Simulation Based Inference (SBI) is shown to yield more accurate resonance parameter estimates than traditional chi-squared minimization in certain cases of model misspecification, demonstrated through a case study of pi-pi scattering and the rho(770) resonance.…

  • A Two-armed Bandit Framework for A/B Testing

    A Two-armed Bandit Framework for A/B Testing arXiv:2507.18118v1 Announce Type: new Abstract: A/B testing is widely used in modern technology companies for policy evaluation and product deployment, with the goal of comparing the outcomes under a newly-developed policy against a standard control. Various causal inference and reinforcement learning methods developed in the literature are applicable…

  • 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…

  • Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics

    Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics arXiv:2506.20935v1 Announce Type: new Abstract: Forecasting geopolitical conflict from data sources like the Global Database of Events, Language, and Tone (GDELT) is a critical challenge for national security. The inherent sparsity, burstiness,…

  • Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies

    Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies arXiv:2506.13955v1 Announce Type: new Abstract: Anomaly detection (AD) is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal data from (synthetic) anomalies. We extend…

  • 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…

  • The Currents of Conflict: Decomposing Conflict Trends with Gaussian Processes

    The Currents of Conflict: Decomposing Conflict Trends with Gaussian Processes arXiv:2506.06828v1 Announce Type: new Abstract: I present a novel approach to estimating the temporal and spatial patterns of violent conflict. I show how we can use highly temporally and spatially disaggregated data on conflict events in tandem with Gaussian processes to estimate temporospatial conflict trends.…

  • MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements

    MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements arXiv:2506.02260v1 Announce Type: new Abstract: The growing prevalence of digital health technologies has led to the generation of complex multi-modal data, such as physical activity measurements simultaneously collected from various sensors of mobile and wearable devices. These data hold immense potential for advancing health studies, but current…

  • Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine

    Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine arXiv:2505.17283v1 Announce Type: new Abstract: Randomized clinical trials often require large patient cohorts before drawing definitive conclusions, yet abundant observational data from parallel studies remains underutilized due to confounding and hidden biases. To bridge this gap, we propose Deconfounded Warm-Start Thompson Sampling (DWTS), a practical approach…

  • Boosting Statistic Learning with Synthetic Data from Pretrained Large Models

    Boosting Statistic Learning with Synthetic Data from Pretrained Large Models arXiv:2505.04992v1 Announce Type: new Abstract: The rapid advancement of generative models, such as Stable Diffusion, raises a key question: how can synthetic data from these models enhance predictive modeling? While they can generate vast amounts of datasets, only a subset meaningfully improves performance. We propose…

  • Fast Likelihood-Free Parameter Estimation for L’evy Processes

    Fast Likelihood-Free Parameter Estimation for L’evy Processes arXiv:2505.01639v1 Announce Type: new Abstract: L’evy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high-frequency asset return data. However, parameter estimation remains a challenge when associated likelihoods are unavailable or costly to compute. We propose…

  • Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model

    Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model arXiv:2504.21795v1 Announce Type: new Abstract: The Hawkes process (HP) is commonly used to model event sequences with self-reinforcing dynamics, including electronic health records (EHRs). Traditional HPs capture self-reinforcement via parametric impact functions that can be inspected to understand how each…

  • Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows

    Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows arXiv:2504.04013v1 Announce Type: new Abstract: Post-earthquake hazard and impact estimation are critical for effective disaster response, yet current approaches face significant limitations. Traditional models employ fixed parameters regardless of geographical context, misrepresenting how seismic effects vary across diverse…

  • Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting

    Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting arXiv:2504.02518v1 Announce Type: new Abstract: Probabilistic electricity price forecasting (PEPF) is a key task for market participants in short-term electricity markets. The increasing availability of high-frequency data and the need for real-time decision-making in energy markets require online estimation methods for efficient model updating.…

  • DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization

    DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization arXiv:2503.23430v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn models that can generalize well to unseen domains by training only on a set of source domains. Sharpness-Aware Minimization (SAM) has been a popular approach for this, aiming to find flat minima in the total loss landscape.…

  • Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis

    Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis arXiv:2503.21802v1 Announce Type: cross Abstract: Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we…

  • Procrustes Wasserstein Metric: A Modified Benamou-Brenier Approach with Applications to Latent Gaussian Distributions

    Procrustes Wasserstein Metric: A Modified Benamou-Brenier Approach with Applications to Latent Gaussian Distributions arXiv:2503.16580v1 Announce Type: new Abstract: We introduce a modified Benamou-Brenier type approach leading to a Wasserstein type distance that allows global invariance, specifically, isometries, and we show that the problem can be summarized to orthogonal transformations. This distance is defined by penalizing…

  • 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…

  • A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation

    A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation arXiv:2503.08902v1 Announce Type: new Abstract: Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, impacting accuracy and robustness. One idea is to use an auxiliary neural network to train an MI…

  • Learning Causal Response Representations through Direct Effect Analysis

    Learning Causal Response Representations through Direct Effect Analysis arXiv:2503.04358v1 Announce Type: new Abstract: We propose a novel approach for learning causal response representations. Our method aims to extract directions in which a multidimensional outcome is most directly caused by a treatment variable. By bridging conditional independence testing with causal representation learning, we formulate an optimisation…

  • Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood

    Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood arXiv:2502.19086v1 Announce Type: new Abstract: We introduce the use of Gaussian Processes (GPs) for the probabilistic forecasting of intermittent time series. The model is trained in a Bayesian framework that accounts for the uncertainty about the latent function and marginalizes it out when making predictions.…

  • Enhancing Gradient-based Discrete Sampling via Parallel Tempering

    Enhancing Gradient-based Discrete Sampling via Parallel Tempering arXiv:2502.19240v1 Announce Type: new Abstract: While gradient-based discrete samplers are effective in sampling from complex distributions, they are susceptible to getting trapped in local minima, particularly in high-dimensional, multimodal discrete distributions, owing to the discontinuities inherent in these landscapes. To circumvent this issue, we combine parallel tempering, also…

  • 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…

  • Variational phylogenetic inference with products over bipartitions

    Variational phylogenetic inference with products over bipartitions arXiv:2502.15110v1 Announce Type: new Abstract: Bayesian phylogenetics requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form…

  • 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…

  • Statistical Verification of Linear Classifiers

    Statistical Verification of Linear Classifiers arXiv:2501.14430v1 Announce Type: new Abstract: We propose a homogeneity test closely related to the concept of linear separability between two samples. Using the test one can answer the question whether a linear classifier is merely “random” or effectively captures differences between two classes. We focus on establishing upper bounds for…

  • 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…

  • Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks

    Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks arXiv:2501.04234v1 Announce Type: new Abstract: Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of downstream tasks. To summarize performance across multiple tasks, evaluation metrics are…

  • 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)…

  • How to Choose a Threshold for an Evaluation Metric for Large Language Models

    How to Choose a Threshold for an Evaluation Metric for Large Language Models arXiv:2412.12148v1 Announce Type: new Abstract: To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a methodology to identify a robust threshold on these metrics even though…

  • GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction

    GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction arXiv:2412.08661v1 Announce Type: new Abstract: Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally…

  • Functional relevance based on the continuous Shapley value

    Functional relevance based on the continuous Shapley value arXiv:2411.18575v1 Announce Type: new Abstract: The presence of Artificial Intelligence (AI) in our society is increasing, which brings with it the need to understand the behaviour of AI mechanisms, including machine learning predictive algorithms fed with tabular data, text, or images, among other types of data. This…

  • 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…