mailitics

Category: stat.ML

  • On Experiments

    On Experiments arXiv:2508.08288v1 Announce Type: new Abstract: The scientific process is a means for turning the results of experiments into knowledge about the world in which we live. Much research effort has been directed toward automating this process. To do this, one needs to formulate the scientific process in a precise mathematical language. This paper…

  • Projection-based multifidelity linear regression for data-scarce applications

    Projection-based multifidelity linear regression for data-scarce applications arXiv:2508.08517v1 Announce Type: new Abstract: Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations…

  • In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality

    In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality arXiv:2508.08673v1 Announce Type: new Abstract: This paper investigates the expected excess risk of In-Context Learning (ICL) for multiclass classification. We model each task as a sequence of labeled prompt samples and a query input, where a pre-trained model estimates the conditional class probabilities of…

  • Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction

    Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction arXiv:2508.08724v1 Announce Type: new Abstract: Recent advances in machine learning have greatly expanded the repertoire of predictive methods for medical imaging. However, the interpretability of complex models remains a challenge, which limits their utility in medical applications. Recently, model-agnostic methods have been proposed to measure…

  • Bio-Inspired Artificial Neural Networks based on Predictive Coding

    Bio-Inspired Artificial Neural Networks based on Predictive Coding arXiv:2508.08762v1 Announce Type: new Abstract: Backpropagation (BP) of errors is the backbone training algorithm for artificial neural networks (ANNs). It updates network weights through gradient descent to minimize a loss function representing the mismatch between predictions and desired outputs. BP uses the chain rule to propagate the…

  • Federated Online Learning for Heterogeneous Multisource Streaming Data

    Federated Online Learning for Heterogeneous Multisource Streaming Data arXiv:2508.06652v1 Announce Type: new Abstract: Federated learning has emerged as an essential paradigm for distributed multi-source data analysis under privacy concerns. Most existing federated learning methods focus on the “static” datasets. However, in many real-world applications, data arrive continuously over time, forming streaming datasets. This introduces additional…

  • MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning

    MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning arXiv:2508.06847v1 Announce Type: new Abstract: High-dimensional Bayesian Optimization (BO) has attracted significant attention in recent research. However, existing methods have mainly focused on optimizing in continuous domains, while combinatorial (ordinal and categorical) and mixed domains still remain challenging. In this paper, we…

  • Membership Inference Attacks with False Discovery Rate Control

    Membership Inference Attacks with False Discovery Rate Control arXiv:2508.07066v1 Announce Type: new Abstract: Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have…

  • Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptation

    Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptation arXiv:2508.07049v1 Announce Type: new Abstract: Anomaly detection (AD) plays a vital role across a wide range of domains, but its performance might deteriorate when applied to target domains with limited data. Domain Adaptation (DA) offers a solution by transferring knowledge from a related source…

  • Stochastic dynamics learning with state-space systems

    Stochastic dynamics learning with state-space systems arXiv:2508.07876v1 Announce Type: new Abstract: This work advances the theoretical foundations of reservoir computing (RC) by providing a unified treatment of fading memory and the echo state property (ESP) in both deterministic and stochastic settings. We investigate state-space systems, a central model class in time series learning, and establish…

  • Random Walk Learning and the Pac-Man Attack

    Random Walk Learning and the Pac-Man Attack arXiv:2508.05663v1 Announce Type: new Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. However, their reliance on local interactions makes them inherently vulnerable to malicious behavior. In this work, we investigate an…

  • Reduction Techniques for Survival Analysis

    Reduction Techniques for Survival Analysis arXiv:2508.05715v1 Announce Type: new Abstract: In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that “reduce” a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival…

  • Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory

    Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory arXiv:2508.05764v1 Announce Type: new Abstract: We consider matrices $boldsymbol{A}(boldsymboltheta)inmathbb{R}^{mtimes m}$ that depend, possibly nonlinearly, on a parameter $boldsymboltheta$ from a compact parameter space $Theta$. We present a Monte Carlo estimator for minimizing $text{trace}(boldsymbol{A}(boldsymboltheta))$ over all $boldsymbolthetainTheta$, and determine the sampling amount so that…

  • Lightweight Auto-bidding based on Traffic Prediction in Live Advertising

    Lightweight Auto-bidding based on Traffic Prediction in Live Advertising arXiv:2508.06069v1 Announce Type: new Abstract: Internet live streaming is widely used in online entertainment and e-commerce, where live advertising is an important marketing tool for anchors. An advertising campaign hopes to maximize the effect (such as conversions) under constraints (such as budget and cost-per-click). The mainstream…

  • 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…

  • Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform

    Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform arXiv:2508.04800v1 Announce Type: new Abstract: We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discovery Rate (FDR) control under differential privacy constraints. While the Model-X knockoff procedure provides FDR guarantees by constructing provably exchangeable “negative control” features, existing privacy mechanisms like…

  • The Cosine Schedule is Fisher-Rao-Optimal for Masked Discrete Diffusion Models

    The Cosine Schedule is Fisher-Rao-Optimal for Masked Discrete Diffusion Models arXiv:2508.04884v1 Announce Type: new Abstract: In this work, we study the problem of choosing the discretisation schedule for sampling from masked discrete diffusion models in terms of the information geometry of the induced probability path. Specifically, we show that the optimal schedule under the Fisher-Rao…

  • L1-Regularized Functional Support Vector Machine

    L1-Regularized Functional Support Vector Machine arXiv:2508.05567v1 Announce Type: new Abstract: In functional data analysis, binary classification with one functional covariate has been extensively studied. We aim to fill in the gap of considering multivariate functional covariates in classification. In particular, we propose an $L_1$-regularized functional support vector machine for binary classification. An accompanying algorithm is…

  • High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference

    High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference arXiv:2508.05212v1 Announce Type: new Abstract: With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogeneous datasets containing sensitive personal information. Differential privacy provides a rigorous framework for safeguarding individual privacy while enabling meaningful statistical analysis. In…

  • High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation

    High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation arXiv:2508.05570v1 Announce Type: new Abstract: In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) averaging under Markovian noise. We focus on the version of the algorithm with constant step size $alpha$ and propose a…

  • Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities

    Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities arXiv:2508.03896v1 Announce Type: new Abstract: The accurate labeling of datasets is often both costly and time-consuming. Given an unlabeled dataset, programmatic weak supervision obtains probabilistic predictions for the labels by leveraging multiple weak labeling functions (LFs) that provide rough guesses for labels. Weak LFs commonly…

  • Reinforcement Learning in MDPs with Information-Ordered Policies

    Reinforcement Learning in MDPs with Information-Ordered Policies arXiv:2508.03904v1 Announce Type: new Abstract: We propose an epoch-based reinforcement learning algorithm for infinite-horizon average-cost Markov decision processes (MDPs) that leverages a partial order over a policy class. In this structure, $pi’ leq pi$ if data collected under $pi$ can be used to estimate the performance of $pi’$,…

  • Deep Neural Network-Driven Adaptive Filtering

    Deep Neural Network-Driven Adaptive Filtering arXiv:2508.04258v1 Announce Type: new Abstract: This paper proposes a deep neural network (DNN)-driven framework to address the longstanding generalization challenge in adaptive filtering (AF). In contrast to traditional AF frameworks that emphasize explicit cost function design, the proposed framework shifts the paradigm toward direct gradient acquisition. The DNN, functioning as…

  • Negative binomial regression and inference using a pre-trained transformer

    Negative binomial regression and inference using a pre-trained transformer arXiv:2508.04111v1 Announce Type: new Abstract: Negative binomial regression is essential for analyzing over-dispersed count data in in comparative studies, but parameter estimation becomes computationally challenging in large screens requiring millions of comparisons. We investigate using a pre-trained transformer to produce estimates of negative binomial regression parameters…

  • The Relative Instability of Model Comparison with Cross-validation

    The Relative Instability of Model Comparison with Cross-validation arXiv:2508.04409v1 Announce Type: new Abstract: Existing work has shown that cross-validation (CV) can be used to provide an asymptotic confidence interval for the test error of a stable machine learning algorithm, and existing stability results for many popular algorithms can be applied to derive positive instances where…

  • A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization

    A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization arXiv:2508.03314v1 Announce Type: new Abstract: The dual formulation of empirical risk minimization with f-divergence regularization (ERM-fDR) is introduced. The solution of the dual optimization problem to the ERM-fDR is connected to the notion of normalization function introduced as an implicit function. This dual approach…

  • Hedging with memory: shallow and deep learning with signatures

    Hedging with memory: shallow and deep learning with signatures arXiv:2508.02759v1 Announce Type: new Abstract: We investigate the use of path signatures in a machine learning context for hedging exotic derivatives under non-Markovian stochastic volatility models. In a deep learning setting, we use signatures as features in feedforward neural networks and show that they outperform LSTMs…

  • Supervised Dynamic Dimension Reduction with Deep Neural Network

    Supervised Dynamic Dimension Reduction with Deep Neural Network arXiv:2508.03546v1 Announce Type: new Abstract: This paper studies the problem of dimension reduction, tailored to improving time series forecasting with high-dimensional predictors. We propose a novel Supervised Deep Dynamic Principal component analysis (SDDP) framework that incorporates the target variable and lagged observations into the factor extraction process.…

  • 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…

  • Learning quadratic neural networks in high dimensions: SGD dynamics and scaling laws

    Learning quadratic neural networks in high dimensions: SGD dynamics and scaling laws arXiv:2508.03688v1 Announce Type: new Abstract: We study the optimization and sample complexity of gradient-based training of a two-layer neural network with quadratic activation function in the high-dimensional regime, where the data is generated as $y propto sum_{j=1}^{r}lambda_j sigmaleft(langle boldsymbol{theta_j}, boldsymbol{x}rangleright), boldsymbol{x} sim N(0,boldsymbol{I}_d)$,…

  • Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling

    Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling arXiv:2508.01217v1 Announce Type: new Abstract: Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we introduce a novel post-processing approach. This approach feeds the output…

  • Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing

    Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing arXiv:2508.01065v1 Announce Type: new Abstract: Motivated by canonical problems in medical diagnostics, we propose and study properties of an objective function that uniformly bounds uncertainties in quantities of interest extracted from classifiers and related data analysis tools. We begin by adopting a set-theoretic…

  • Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables

    Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables arXiv:2508.01321v1 Announce Type: new Abstract: To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains an elusive task. In this paper, we make progress on this task by utilizing instrumental variables (IVs). IVs are…

  • Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis

    Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis arXiv:2508.01341v1 Announce Type: new Abstract: Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can…

  • 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…

  • funOCLUST: Clustering Functional Data with Outliers

    funOCLUST: Clustering Functional Data with Outliers arXiv:2508.00110v1 Announce Type: new Abstract: Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the functional setting is proposed to address these issues. The approach leverages the OCLUST framework, creating a robust method to…

  • Sinusoidal Approximation Theorem for Kolmogorov-Arnold Networks

    Sinusoidal Approximation Theorem for Kolmogorov-Arnold Networks arXiv:2508.00247v1 Announce Type: new Abstract: The Kolmogorov-Arnold representation theorem states that any continuous multivariable function can be exactly represented as a finite superposition of continuous single variable functions. Subsequent simplifications of this representation involve expressing these functions as parameterized sums of a smaller number of unique monotonic functions. These…

  • DO-EM: Density Operator Expectation Maximization

    DO-EM: Density Operator Expectation Maximization arXiv:2507.22786v1 Announce Type: cross Abstract: Density operators, quantum generalizations of probability distributions, are gaining prominence in machine learning due to their foundational role in quantum computing. Generative modeling based on density operator models (textbf{DOMs}) is an emerging field, but existing training algorithms — such as those for the Quantum Boltzmann…

  • 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…

  • 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…

  • A Smoothing Newton Method for Rank-one Matrix Recovery

    A Smoothing Newton Method for Rank-one Matrix Recovery arXiv:2507.23017v1 Announce Type: new Abstract: We consider the phase retrieval problem, which involves recovering a rank-one positive semidefinite matrix from rank-one measurements. A recently proposed algorithm based on Bures-Wasserstein gradient descent (BWGD) exhibits superlinear convergence, but it is unstable, and existing theory can only prove local linear…

  • Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rules

    Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rules arXiv:2507.23349v1 Announce Type: new Abstract: Individualized treatment rules (ITRs) have gained significant attention due to their wide-ranging applications in fields such as precision medicine, ridesharing, and advertising recommendations. However, when ITRs are influenced by sensitive attributes such as race, gender, or age, they…

  • DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction

    DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction arXiv:2507.23736v1 Announce Type: new Abstract: Access to medical imaging and associated text data has the potential to drive major advances in healthcare research and patient outcomes. However, the presence of Protected Health Information (PHI) and Personally Identifiable Information (PII) in Digital Imaging and…

  • Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing

    Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing arXiv:2507.23767v1 Announce Type: new Abstract: A novel approach is presented for identifying distinct signatures of performing acts in the secondary ticket resale market by analyzing dynamic pricing distributions. Using a newly curated, time series dataset from the SeatGeek API, we model ticket pricing distributions as…

  • Formal Bayesian Transfer Learning via the Total Risk Prior

    Formal Bayesian Transfer Learning via the Total Risk Prior arXiv:2507.23768v1 Announce Type: new Abstract: In analyses with severe data-limitations, augmenting the target dataset with information from ancillary datasets in the application domain, called source datasets, can lead to significantly improved statistical procedures. However, existing methods for this transfer learning struggle to deal with situations where…

  • Simulating Posterior Bayesian Neural Networks with Dependent Weights

    Simulating Posterior Bayesian Neural Networks with Dependent Weights arXiv:2507.22095v1 Announce Type: new Abstract: In this paper we consider posterior Bayesian fully connected and feedforward deep neural networks with dependent weights. Particularly, if the likelihood is Gaussian, we identify the distribution of the wide width limit and provide an algorithm to sample from the network. In…

  • 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…

  • LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process

    LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process arXiv:2507.22493v1 Announce Type: new Abstract: We propose a novel probabilistic framework, termed LVM-GP, for uncertainty quantification in solving forward and inverse partial differential equations (PDEs) with noisy data. The core idea is to construct a stochastic mapping from the input to a high-dimensional…

  • Subgrid BoostCNN: Efficient Boosting of Convolutional Networks via Gradient-Guided Feature Selection

    Subgrid BoostCNN: Efficient Boosting of Convolutional Networks via Gradient-Guided Feature Selection arXiv:2507.22842v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep architectures. However, the large number of layers and millions of parameters often make CNNs computationally expensive…

  • A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation

    A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation arXiv:2507.22632v1 Announce Type: new Abstract: Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels. While the field has seen extensive methodological development, its theoretical foundations remain relatively underexplored.…

  • Graph neural networks for residential location choice: connection to classical logit models

    Graph neural networks for residential location choice: connection to classical logit models arXiv:2507.21334v1 Announce Type: new Abstract: Researchers have adopted deep learning for classical discrete choice analysis as it can capture complex feature relationships and achieve higher predictive performance. However, the existing deep learning approaches cannot explicitly capture the relationship among choice alternatives, which has…

  • From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions

    From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions arXiv:2507.21429v1 Announce Type: new Abstract: The convergence of gradient descent (GD) on the non-convex loss landscapes of deep neural networks (DNNs) presents a fundamental theoretical challenge. While recent work has established that GD converges to a stationary point at a sublinear rate…

  • From Global to Local: A Scalable Benchmark for Local Posterior Sampling

    From Global to Local: A Scalable Benchmark for Local Posterior Sampling arXiv:2507.21449v1 Announce Type: new Abstract: Degeneracy is an inherent feature of the loss landscape of neural networks, but it is not well understood how stochastic gradient MCMC (SGMCMC) algorithms interact with this degeneracy. In particular, current global convergence guarantees for common SGMCMC algorithms rely…

  • Measuring Sample Quality with Copula Discrepancies

    Measuring Sample Quality with Copula Discrepancies arXiv:2507.21434v1 Announce Type: new Abstract: The scalable Markov chain Monte Carlo (MCMC) algorithms that underpin modern Bayesian machine learning, such as Stochastic Gradient Langevin Dynamics (SGLD), sacrifice asymptotic exactness for computational speed, creating a critical diagnostic gap: traditional sample quality measures fail catastrophically when applied to biased samplers. While…

  • Stochastic forest transition model dynamics and parameter estimation via deep learning

    Stochastic forest transition model dynamics and parameter estimation via deep learning arXiv:2507.21486v1 Announce Type: new Abstract: Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions…

  • Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective

    Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective arXiv:2507.19540v1 Announce Type: new Abstract: Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we…

  • Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices

    Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices arXiv:2507.19663v1 Announce Type: new Abstract: Solder joint reliability related to failures due to thermomechanical loading is a critically important yet physically complex engineering problem. As a result, simulated behavior is oftentimes computationally expensive. In an increasingly data-driven world, the usage of efficient data-driven design…

  • Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures

    Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures arXiv:2507.19787v1 Announce Type: new Abstract: The dynamic mode decomposition (DMD) is a data-driven approach that extracts the dominant features from spatiotemporal data. In this work, we introduce sparse-mode DMD, a new variant of the optimized DMD framework that specifically leverages sparsity-promoting regularization in order to…

  • Bag of Coins: A Statistical Probe into Neural Confidence Structures

    Bag of Coins: A Statistical Probe into Neural Confidence Structures arXiv:2507.19774v1 Announce Type: new Abstract: Modern neural networks, despite their high accuracy, often produce poorly calibrated confidence scores, limiting their reliability in high-stakes applications. Existing calibration methods typically post-process model outputs without interrogating the internal consistency of the predictions themselves. In this work, we introduce…

  • 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…

  • Central limit theorems for the eigenvalues of graph Laplacians on data clouds

    Central limit theorems for the eigenvalues of graph Laplacians on data clouds arXiv:2507.18803v1 Announce Type: new Abstract: Given i.i.d. samples $X_n ={ x_1, dots, x_n }$ from a distribution supported on a low dimensional manifold ${M}$ embedded in Eucliden space, we consider the graph Laplacian operator $Delta_n$ associated to an $varepsilon$-proximity graph over $X_n$ and…

  • 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…

  • Probably Approximately Correct Causal Discovery

    Probably Approximately Correct Causal Discovery arXiv:2507.18903v1 Announce Type: new Abstract: The discovery of causal relationships is a foundational problem in artificial intelligence, statistics, epidemiology, economics, and beyond. While elegant theories exist for accurate causal discovery given infinite data, real-world applications are inherently resource-constrained. Effective methods for inferring causal relationships from observational data must perform well…

  • 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.…

  • Flow Stochastic Segmentation Networks

    Flow Stochastic Segmentation Networks arXiv:2507.18838v1 Announce Type: cross Abstract: We introduce the Flow Stochastic Segmentation Network (Flow-SSN), a generative segmentation model family featuring discrete-time autoregressive and modern continuous-time flow variants. We prove fundamental limitations of the low-rank parameterisation of previous methods and show that Flow-SSNs can estimate arbitrarily high-rank pixel-wise covariances without assuming the rank…

  • 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…

  • Learning graphons from data: Random walks, transfer operators, and spectral clustering

    Learning graphons from data: Random walks, transfer operators, and spectral clustering arXiv:2507.18147v1 Announce Type: new Abstract: Many signals evolve in time as a stochastic process, randomly switching between states over discretely sampled time points. Here we make an explicit link between the underlying stochastic process of a signal that can take on a bounded continuum…

  • 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…

  • On Reconstructing Training Data From Bayesian Posteriors and Trained Models

    On Reconstructing Training Data From Bayesian Posteriors and Trained Models arXiv:2507.18372v1 Announce Type: new Abstract: Publicly releasing the specification of a model with its trained parameters means an adversary can attempt to reconstruct information about the training data via training data reconstruction attacks, a major vulnerability of modern machine learning methods. This paper makes three…

  • DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts

    DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts arXiv:2507.18464v1 Announce Type: new Abstract: Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or simple voting schemes that fail to optimally leverage specialized knowledge. This…

  • Fundamental limits of distributed covariance matrix estimation via a conditional strong data processing inequality

    Fundamental limits of distributed covariance matrix estimation via a conditional strong data processing inequality arXiv:2507.16953v1 Announce Type: new Abstract: Estimating high-dimensional covariance matrices is a key task across many fields. This paper explores the theoretical limits of distributed covariance estimation in a feature-split setting, where communication between agents is constrained. Specifically, we study a scenario…

  • Bayesian preference elicitation for decision support in multiobjective optimization

    Bayesian preference elicitation for decision support in multiobjective optimization arXiv:2507.16999v1 Announce Type: new Abstract: We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesian model to estimate the decision-maker’s utility function based on pairwise comparisons. Aided by this model,…

  • The surprising strength of weak classifiers for validating neural posterior estimates

    The surprising strength of weak classifiers for validating neural posterior estimates arXiv:2507.17026v1 Announce Type: new Abstract: Neural Posterior Estimation (NPE) has emerged as a powerful approach for amortized Bayesian inference when the true posterior $p(theta mid y)$ is intractable or difficult to sample. But evaluating the accuracy of neural posterior estimates remains challenging, with existing…

  • CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates

    CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates arXiv:2507.17030v1 Announce Type: new Abstract: We consider the problem of validating whether a neural posterior estimate ( q(theta mid x) ) is an accurate approximation to the true, unknown true posterior ( p(theta mid x) ). Existing methods for evaluating the quality…

  • Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability

    Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability arXiv:2507.17316v1 Announce Type: new Abstract: We consider the problem of estimating a discrete distribution $p$ with support of size $K$ and provide both upper and lower bounds with high probability in KL divergence. We prove that in the worst case, for any estimator $widehat{p}$,…

  • Structural DID with ML: Theory, Simulation, and a Roadmap for Applied Research

    Structural DID with ML: Theory, Simulation, and a Roadmap for Applied Research arXiv:2507.15899v1 Announce Type: new Abstract: Causal inference in observational panel data has become a central concern in economics,policy analysis,and the broader social sciences.To address the core contradiction where traditional difference-in-differences (DID) struggles with high-dimensional confounding variables in observational panel data,while machine learning (ML)…

  • Generative AI Models for Learning Flow Maps of Stochastic Dynamical Systems in Bounded Domains

    Generative AI Models for Learning Flow Maps of Stochastic Dynamical Systems in Bounded Domains arXiv:2507.15990v1 Announce Type: new Abstract: Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in…

  • Estimating Treatment Effects with Independent Component Analysis

    Estimating Treatment Effects with Independent Component Analysis arXiv:2507.16467v1 Announce Type: new Abstract: The field of causal inference has developed a variety of methods to accurately estimate treatment effects in the presence of nuisance. Meanwhile, the field of identifiability theory has developed methods like Independent Component Analysis (ICA) to identify latent sources and mixing weights from…

  • PAC Off-Policy Prediction of Contextual Bandits

    PAC Off-Policy Prediction of Contextual Bandits arXiv:2507.16236v1 Announce Type: new Abstract: This paper investigates off-policy evaluation in contextual bandits, aiming to quantify the performance of a target policy using data collected under a different and potentially unknown behavior policy. Recently, methods based on conformal prediction have been developed to construct reliable prediction intervals that guarantee…

  • 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.…

  • Statistical and Algorithmic Foundations of Reinforcement Learning

    Statistical and Algorithmic Foundations of Reinforcement Learning arXiv:2507.14444v1 Announce Type: new Abstract: As a paradigm for sequential decision making in unknown environments, reinforcement learning (RL) has received a flurry of attention in recent years. However, the explosion of model complexity in emerging applications and the presence of nonconvexity exacerbate the challenge of achieving efficient RL…

  • Diffusion Models for Time Series Forecasting: A Survey

    Diffusion Models for Time Series Forecasting: A Survey arXiv:2507.14507v1 Announce Type: new Abstract: Diffusion models, initially developed for image synthesis, demonstrate remarkable generative capabilities. Recently, their application has expanded to time series forecasting (TSF), yielding promising results. In this survey, we firstly introduce the standard diffusion models and their prevalent variants, explaining their adaptation to…

  • Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction

    Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction arXiv:2507.14641v1 Announce Type: new Abstract: This research integrates deep learning, copula functions, and survival analysis to effectively handle highly correlated and right-censored multivariate survival data. It introduces copula-based activation functions (Clayton, Gumbel, and their combinations) to model the nonlinear dependencies inherent in such…

  • When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

    When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts arXiv:2507.14661v1 Announce Type: new Abstract: Semi-supervised domain adaptation (SSDA) aims to achieve high predictive performance in the target domain with limited labeled target data by exploiting abundant source and unlabeled target data. Despite its significance in numerous…

  • Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators

    Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators arXiv:2507.14652v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s…

  • Differential Privacy in Kernelized Contextual Bandits via Random Projections

    Differential Privacy in Kernelized Contextual Bandits via Random Projections arXiv:2507.13639v1 Announce Type: new Abstract: We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space. We study this problem under an additional constraint of Differential Privacy, where the agent needs to ensure…

  • Conformal Data Contamination Tests for Trading or Sharing of Data

    Conformal Data Contamination Tests for Trading or Sharing of Data arXiv:2507.13835v1 Announce Type: new Abstract: The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can be expanded through trading or sharing with external data agents. However, data buyers need…

  • A Survey of Dimension Estimation Methods

    A Survey of Dimension Estimation Methods arXiv:2507.13887v1 Announce Type: new Abstract: It is a standard assumption that datasets in high dimension have an internal structure which means that they in fact lie on, or near, subsets of a lower dimension. In many instances it is important to understand the real dimension of the data, hence…

  • Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design

    Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design arXiv:2507.14057v1 Announce Type: new Abstract: We develop a semi-amortized, policy-based, approach to Bayesian experimental design (BED) called Stepwise Deep Adaptive Design (Step-DAD). Like existing, fully amortized, policy-based BED approaches, Step-DAD trains a design policy upfront before the experiment. However, rather than keeping this policy fixed, Step-DAD periodically updates it…

  • 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…

  • Physics constrained learning of stochastic characteristics

    Physics constrained learning of stochastic characteristics arXiv:2507.12661v1 Announce Type: new Abstract: Accurate state estimation requires careful consideration of uncertainty surrounding the process and measurement models; these characteristics are usually not well-known and need an experienced designer to select the covariance matrices. An error in the selection of covariance matrices could impact the accuracy of the…

  • Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights

    Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights arXiv:2507.12686v1 Announce Type: new Abstract: We study the Finite-Dimensional Distributions (FDDs) of deep neural networks with randomly initialized weights that have finite-order moments. Specifically, we establish Gaussian approximation bounds in the Wasserstein-$1$ norm between the FDDs and their Gaussian limit assuming a Lipschitz activation…

  • Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effect

    Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effect arXiv:2507.12818v1 Announce Type: new Abstract: In observational studies, confounding variables affect both treatment and outcome. Moreover, instrumental variables also influence the treatment assignment mechanism. This situation sets the study apart from a standard randomized controlled trial, where the treatment assignment is random. Due…

  • Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes

    Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes arXiv:2507.12878v1 Announce Type: new Abstract: The identification of Linear Time-Variant (LTV) systems from input-output data is a fundamental yet challenging ill-posed inverse problem. This work introduces a unified Bayesian framework that models the system’s impulse response, $h(t, tau)$, as a stochastic…

  • When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values

    When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values arXiv:2507.13024v1 Announce Type: new Abstract: Predicting a response with partially missing inputs remains a challenging task even in parametric models, since parameter estimation in itself is not sufficient to predict on partially observed inputs. Several works study prediction in linear models. In…

  • LLMs are Bayesian, in Expectation, not in Realization

    LLMs are Bayesian, in Expectation, not in Realization arXiv:2507.11768v1 Announce Type: new Abstract: Large language models demonstrate remarkable in-context learning capabilities, adapting to new tasks without parameter updates. While this phenomenon has been successfully modeled as implicit Bayesian inference, recent empirical findings reveal a fundamental contradiction: transformers systematically violate the martingale property, a cornerstone requirement…

  • Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?

    Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work? arXiv:2507.11891v1 Announce Type: new Abstract: We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has shown that the standard difference-in-means estimator is biased in estimating the global treatment effect (GTE) due to a particular…

  • 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…

  • Incorporating Fairness Constraints into Archetypal Analysis

    Incorporating Fairness Constraints into Archetypal Analysis arXiv:2507.12021v1 Announce Type: new Abstract: Archetypal Analysis (AA) is an unsupervised learning method that represents data as convex combinations of extreme patterns called archetypes. While AA provides interpretable and low-dimensional representations, it can inadvertently encode sensitive attributes, leading to fairness concerns. In this work, we propose Fair Archetypal Analysis…

  • Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators

    Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators arXiv:2507.11574v1 Announce Type: cross Abstract: Robust uncertainty quantification (UQ) remains a critical barrier to the safe deployment of deep learning in real-time virtual sensing, particularly in high-stakes domains where sparse, noisy, or non-collocated sensor data are the norm. We introduce the Conformalized Monte Carlo Operator (CMCO), a…