arXiv subjects

Wenguang Sun

Publications and source records attributed to Wenguang Sun.

At least 19 recordsLinked to original sources

arXiv2026

COINS: Any-Stage-Valid and Utility-Oriented Sequential Conformal Prediction

Many predictive workflows update uncertainty as information is acquired and use intermediate reports to determine whether to stop or deploy further resources. We study conformal inference in this setting, treating the resulting prediction sequence as the inferential object. We require any-stage validity, which protects against miscoverage at any inspected stage, and use process-level utility to evaluate how efficiently the sequence supports downstream action. We propose a universal structural theory for constructing any-stage valid prediction sequences. Guided by it, we develop COINS, which coordinates calibration across stages by investing a common finite-sample rejection-count budget only among surviving augmented observations. Under exchangeability, COINS achieves finite-sample any-stage validity and produces prediction sets no larger than their matched Bonferroni counterparts at every stage. We further develop Vopt-COINS, which learns the stagewise allocation for a specified process-level utility, together with branchwise and localized extensions for heterogeneous acquisition pathways and test units. Simulations and a dermatological-diagnosis application confirm any-stage validity and demonstrate gains over Bonferroni and fixed allocations. The proposed methods also perform favorably in ordered score aggregation, viewed as a terminal-utility special case.

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arXiv2026

Structure-Adaptive E-Value Filter for Detecting Regional Signals in Brain Imaging

Structural MRI provides a noninvasive view of the neuroanatomical differences associated with cognitive impairment and dementia. Using data from the Alzheimer's Disease Neuroimaging Initiative, we investigate which anatomical regions exhibit widespread gray-matter differences and how these regional patterns vary across the clinical spectrum. Addressing this goal requires translating spatially dependent voxel-level evidence into regional conclusions while controlling multiplicity across anatomical regions. We propose the Structure-adaptive E-value FilTer (SEFT), which uses flexible working models to construct spatially adaptive voxel-level scores and aggregates them into regional partial-conjunction e-values. When combined with the e-value Benjamini--Hochberg (e-BH) procedure, these e-values provide finite-sample control of the set-wise false discovery rate under arbitrary interregional dependence. The ADNI analysis reveals a coherent neuroanatomical pattern: the exploratory analysis shows that differences between normal cognition and mild cognitive impairment are concentrated in medial-temporal regions, whereas the differences between mild cognitive impairment and dementia extend more broadly into temporal--limbic and posterior association regions. Both patterns largely overlap the normal-cognition--dementia benchmark, identifying a shared anatomical core across the clinical comparisons.

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arXiv2026

Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing

This paper addresses structured out-of-distribution (OOD) testing in high-stakes machine learning applications. Traditional conformal methods rely on joint exchangeability, making it difficult to incorporate auxiliary information such as spatiotemporal or grouping structures. To overcome this limitation, we propose the structure-adaptive conformal q-value (SCQ), a significance index that integrates individual test evidence with structural patterns. We also develop pseudo-score-guided transductive automated model selection (P-TAMS), which adapts conformalized model selection to structured OOD testing across a toolbox of candidate models. Together, SCQ and P-TAMS form a unified framework under pairwise exchangeability, providing finite-sample error-rate control, improved power, and enhanced interpretability. Experiments on simulated and real data demonstrate that the proposed approach controls the false discovery rate and performs well across diverse settings.

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arXiv2026

Conformalized Large-Scale Selective Inference with Informative and Trustworthy Prediction Sets

In large-scale prediction problems, exhaustively following up on all test units is often impractical and inefficient, motivating a selective reporting strategy that fulfills the dual requirements of informativeness and trustworthiness. Within the InfoFCR (Informative prediction with False Coverage Rate control) framework, we propose SCIP (Selective Conformal Inference for Informative Predictions), a procedure built on three key components: (i) an informative set constructor that tailors prediction sets to individual test units according to user-specified informativeness constraints; (ii) a trust score that provides a principled quantification of the trustworthiness of candidate informative sets; and (iii) generalized conformal p-values that are used to perform FCR analysis for selecting the most promising candidates. We establish that SCIP guarantees finite-sample FCR control and is asymptotically anti-conservative, achieving higher statistical power than existing methods. The framework is highly versatile, accommodating a wide range of error metrics across both regression and classification tasks. Extensive numerical experiments on simulated and real data demonstrate the effectiveness of our approach.

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arXiv2025

Safe, Always-Valid Alpha-Investing Rules For Doubly Sequential Online Inference

Dynamic decision-making in rapidly evolving research domains, including marketing, finance, and pharmaceutical development, presents a significant challenge. Researchers frequently confront the need for real-time action within a doubly sequential framework characterized by the continuous influx of high-volume data streams and the intermittent arrival of novel tasks. This calls for the development and implementation of new online inference protocols capable of handling both the continuous processing of incoming information and the efficient allocation of resources to address emerging priorities. We introduce a novel class of Safe and Always-Valid Alpha-investing (SAVA) rules that leverages powerful tools including always valid p-values, e-processes, and online false discovery rate methods. The SAVA algorithm effectively integrates information across all tasks, mitigates the alpha-death problem, and controls the false selection rate (FSR) at all decision points. We validate the efficacy of the SAVA framework through rigorous theoretical analysis and extensive numerical experiments. Our results demonstrate that SAVA not only offers effective control of the FSR but also significantly improves statistical power compared to traditional online testing approaches.

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arXiv2025

Conformalized Multiple Testing under Unknown Null Distribution with Symmetric Errors

This article addresses a fundamental concern, first raised by Efron (2004), regarding the selection of null distributions in large-scale multiple testing. In modern data-intensive applications involving thousands or even millions of hypotheses, the theoretical null distribution of the test statistics often deviates from the true underlying null distribution, severely compromising the false discovery rate (FDR) analysis. We propose a conformalized empirical Bayes method using self-calibrated empirical null samples (SENS) for both one-sample and two-sample multiple testing problems. The new framework not only sidesteps the use of potentially erroneous theoretical null distributions, which is common in conventional practice, but also mitigates the impact of estimation errors in the unknown null distribution on the validity of FDR control, a challenge frequently encountered in the empirical Bayes FDR literature. In contrast to the empirical Bayes approaches (cf. Efron, 2004; Jin and Cai, 2007; Sun and Cai, 2007) that rely on Gaussian assumptions for the null models, SENS imposes only a weak condition on the symmetry of the error distribution, and leverages conformal tools to achieve FDR control in finite samples. Moreover, SENS incorporates structural insights from empirical Bayes into inference, exhibiting higher power compared to frequentist model-free methods. We conduct an in-depth analysis to establish a novel optimality theory for SENS under Efron's two-group model and demonstrate its superiority over existing empirical Bayes FDR methods and recent model-free FDR methods through numerical experiments on both simulated and real data.

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arXiv2025

A Conformalized Empirical Bayes Method for Multiple Testing with Side Information

This article presents a Conformalized Locally Adaptive Weighting (CLAW) approach to multiple testing with side information. The proposed method employs innovative data-driven strategies to construct pairwise exchangeable scores, which are integrated into a generic algorithm that leverages a mirror process for controlling the false discovery rate (FDR). By combining principles from empirical Bayes with powerful techniques in conformal inference, CLAW provides a valid and efficient framework for incorporating structural information from both test data and auxiliary covariates. Unlike existing empirical Bayes FDR methods that primarily offer asymptotic validity, often under strong regularity conditions, CLAW controls the FDR in finite samples under weaker conditions. Extensive numerical studies using both simulated and real data demonstrate that CLAW exhibits superior performance compared to existing methods.

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arXiv2025

Exploring the Noise Robustness of Online Conformal Prediction

Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Recent work develops online conformal prediction methods that adaptively construct prediction sets to accommodate distribution shifts. However, existing algorithms typically assume perfect label accuracy which rarely holds in practice. In this work, we investigate the robustness of online conformal prediction under uniform label noise with a known noise rate, in both constant and dynamic learning rate schedules. We show that label noise causes a persistent gap between the actual mis-coverage rate and the desired rate $\alpha$, leading to either overestimated or underestimated coverage guarantees. To address this issue, we propose Noise Robust Online Conformal Prediction (dubbed NR-OCP) by updating the threshold with a novel robust pinball loss, which provides an unbiased estimate of clean pinball loss without requiring ground-truth labels. Our theoretical analysis shows that NR-OCP eliminates the coverage gap in both constant and dynamic learning rate schedules, achieving a convergence rate of $\mathcal{O}(T^{-1/2})$ for both empirical and expected coverage errors under uniform label noise. Extensive experiments demonstrate the effectiveness of our method by achieving both precise coverage and improved efficiency.

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arXiv2023

Cotton Yield Prediction Using Random Forest

The cotton industry in the United States is committed to sustainable production practices that minimize water, land, and energy use while improving soil health and cotton output. Climate-smart agricultural technologies are being developed to boost yields while decreasing operating expenses. Crop yield prediction, on the other hand, is difficult because of the complex and nonlinear impacts of cultivar, soil type, management, pest and disease, climate, and weather patterns on crops. To solve this issue, we employ machine learning (ML) to forecast production while considering climate change, soil diversity, cultivar, and inorganic nitrogen levels. From the 1980s to the 1990s, field data were gathered across the southern cotton belt of the United States. To capture the most current effects of climate change over the previous six years, a second data source was produced using the process-based crop model, GOSSYM. We concentrated our efforts on three distinct areas inside each of the three southern states: Texas, Mississippi, and Georgia. To simplify the amount of computations, accumulated heat units (AHU) for each set of experimental data were employed as an analogy to use time-series weather data. The Random Forest Regressor yielded a 97.75% accuracy rate, with a root mean square error of 55.05 kg/ha and an R2 of around 0.98. These findings demonstrate how an ML technique may be developed and applied as a reliable and easy-to-use model to support the cotton climate-smart initiative.

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arXiv2023

False Discovery Rate Control For Structured Multiple Testing: Asymmetric Rules And Conformal Q-values

The effective utilization of structural information in data while ensuring statistical validity poses a significant challenge in false discovery rate (FDR) analyses. Conformal inference provides rigorous theory for grounding complex machine learning methods without relying on strong assumptions or highly idealized models. However, existing conformal methods have limitations in handling structured multiple testing. This is because their validity requires the deployment of symmetric rules, which assume the exchangeability of data points and permutation-invariance of fitting algorithms. To overcome these limitations, we introduce the pseudo local index of significance (PLIS) procedure, which is capable of accommodating asymmetric rules and requires only pairwise exchangeability between the null conformity scores. We demonstrate that PLIS offers finite-sample guarantees in FDR control and the ability to assign higher weights to relevant data points. Numerical results confirm the effectiveness and robustness of PLIS and show improvements in power compared to existing model-free methods in various scenarios.

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arXiv2023

Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach

We investigate the problem of compound estimation of normal means while accounting for the presence of side information. Leveraging the empirical Bayes framework, we develop a nonparametric integrative Tweedie (NIT) approach that incorporates structural knowledge encoded in multivariate auxiliary data to enhance the precision of compound estimation. Our approach employs convex optimization tools to estimate the gradient of the log-density directly, enabling the incorporation of structural constraints. We conduct theoretical analyses of the asymptotic risk of NIT and establish the rate at which NIT converges to the oracle estimator. As the dimension of the auxiliary data increases, we accurately quantify the improvements in estimation risk and the associated deterioration in convergence rate. The numerical performance of NIT is illustrated through the analysis of both simulated and real data, demonstrating its superiority over existing methods.

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arXiv2023

Ranking and Selection in Large-Scale Inference of Heteroscedastic Units

The allocation of limited resources to a large number of potential candidates presents a pervasive challenge. In the context of ranking and selecting top candidates from heteroscedastic units, conventional methods often result in over-representations of subpopulations, and this issue is further exacerbated in large-scale settings where thousands of candidates are considered simultaneously. To address this challenge, we propose a new multiple comparison framework that incorporates a modified power notion to prioritize the selection of important effects and employs a novel ranking metric to assess the relative importance of units. We develop both oracle and data-driven algorithms, and demonstrate their effectiveness in controlling the error rates and achieving optimality. We evaluate the numerical performance of our proposed method using simulated and real data. The results show that our framework enables a more balanced selection of effects that are both statistically significant and practically important, and results in an objective and relevant ranking scheme that is well-suited to practical scenarios.

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arXiv2022

A Locally Adaptive Shrinkage Approach to False Selection Rate Control in High-Dimensional Classification

The uncertainty quantification and error control of classifiers are crucial in many high-consequence decision-making scenarios. We propose a selective classification framework that provides an indecision option for any observations that cannot be classified with confidence. The false selection rate (FSR), defined as the expected fraction of erroneous classifications among all definitive classifications, provides a useful error rate notion that trades off a fraction of indecisions for fewer classification errors. We develop a new class of locally adaptive shrinkage and selection (LASS) rules for FSR control in the context of high-dimensional linear discriminant analysis (LDA). LASS is easy-to-analyze and has robust performance across sparse and dense regimes. Theoretical guarantees on FSR control are established without strong assumptions on sparsity as required by existing theories in high-dimensional LDA. The empirical performances of LASS are investigated using both simulated and real data.

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arXiv2022

Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers

This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set. Blending inductive and transductive conformal inference in an innovative way, the described methods can re-weight standard conformal p-values based on dependent side information from known out-of-distribution data in a principled way, and can automatically take advantage of the most powerful model from any collection of one-class and binary classifiers. The solution can be implemented either through sample splitting or via a novel transductive cross-validation+ scheme which may also be useful in other applications of conformal inference, due to tighter guarantees compared to existing cross-validation approaches. After studying false discovery rate control and power within a multiple testing framework with several possible outliers, the proposed solution is shown to outperform standard conformal p-values through simulations as well as applications to image recognition and tabular data.

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arXiv2022

A Locally Adaptive Algorithm for Multiple Testing with Network Structure

Incorporating auxiliary information alongside primary data can significantly enhance the accuracy of simultaneous inference. However, existing multiple testing methods face challenges in efficiently incorporating complex side information, especially when it differs in dimension or structure from the primary data, such as network side information. This paper introduces a locally adaptive structure learning algorithm (LASLA), a flexible framework designed to integrate a broad range of auxiliary information into the inference process. Although LASLA is specifically motivated by the challenges posed by network-structured data, it also proves highly effective with other types of side information, such as spatial locations and multiple auxiliary sequences. LASLA employs a $p$-value weighting approach, leveraging structural insights to derive data-driven weights that prioritize the importance of different hypotheses. Our theoretical analysis demonstrates that LASLA asymptotically controls the false discovery rate (FDR) under independent or weakly dependent $p$-values, and achieves enhanced power in scenarios where the auxiliary data provides valuable side information. Simulation studies are conducted to evaluate LASLA's numerical performance, and its efficacy is further illustrated through two real-world applications.

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arXiv2021

An Empirical Bayes Approach to Controlling the False Discovery Exceedance

In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.

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arXiv2021

A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification

We investigate the fairness issue in classification, where automated decisions are made for individuals from different protected groups. In high-consequence scenarios, decision errors can disproportionately affect certain protected groups, leading to unfair outcomes. To address this issue, we propose a fairness-adjusted selective inference (FASI) framework and develop data-driven algorithms that achieve statistical parity by controlling the false selection rate (FSR) among protected groups. Our FASI algorithm operates by converting the outputs of black-box classifiers into R-values, which are both intuitive and computationally efficient. These R-values serve as the basis for selection rules that are provably valid for FSR control in finite samples for protected groups, effectively mitigating the unfairness in group-wise error rates. We demonstrate the numerical performance of our approach using both simulated and real data.

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arXiv2021

ZAP: $Z$-value Adaptive Procedures for False Discovery Rate Control with Side Information

Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years. It has been widely recognized that leveraging side information provided by auxiliary covariates can improve the power of false discovery rate (FDR) procedures. Currently, most such procedures are devised with $p$-values as their main statistics. However, for two-sided hypotheses, the usual data processing step that transforms the primary statistics, known as $z$-values, into $p$-values not only leads to a loss of information carried by the main statistics, but can also undermine the ability of the covariates to assist with the FDR inference. We develop a $z$-value based covariate-adaptive (ZAP) methodology that operates on the intact structural information encoded jointly by the $z$-values and covariates. It seeks to emulate the oracle $z$-value procedure via a working model, and its rejection regions significantly depart from those of the $p$-value adaptive testing approaches. The key strength of ZAP is that the FDR control is guaranteed with minimal assumptions, even when the working model is misspecified. We demonstrate the state-of-the-art performance of ZAP using both simulated and real data, which shows that the efficiency gain can be substantial in comparison with $p$-value based methods. Our methodology is implemented in the $\texttt{R}$ package $\texttt{zap}$.

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