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George Mason University

Academic institutionnorthamerica · us
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Research library645linked papers
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Selected work

Representative Papers

Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series

Feb 14, 2026SDM

Existing time series chain methods are confined to individual sequences and struggle to capture anomalous evolution patterns across interruptions or related sequences. This work proposes a novel formulation—Joint Time Series Chain—that extends the concept of time series chains to cross-sequence scenarios for the first time. By leveraging subsequence similarity modeling, a cross-sequence alignment strategy, and a new chain-ranking criterion, the method efficiently uncovers robust anomalous evolution trends. Empirical evaluations demonstrate that the proposed approach significantly outperforms current state-of-the-art techniques across multiple datasets. Furthermore, it has been successfully deployed in an Intel manufacturing setting, where it effectively identifies complex anomalous patterns, showcasing its practical utility in real-world industrial applications.

6 citationsRead paper

Seeding with Differentially Private Network Information

May 26, 2023arXiv.org

In public health applications such as HIV prevention, complete behavioral contact networks are often unavailable; only privacy-sensitive, sequential contact samples can be obtained. Method: This paper introduces the first differentially private influence maximization seeding algorithm, supporting both centralized and local privacy models. It integrates randomized data collection, cascade-based influence estimation from sampled cascades, and rigorous theoretical analysis of estimation error bounds—ensuring performance guarantees under limited samples. Contribution/Results: Experiments show that under centralized differential privacy, algorithmic performance degrades gracefully as the privacy budget decreases; under local differential privacy, a larger budget is required to maintain effectiveness—consistent with theoretical predictions. This work provides the first solution for identifying high-impact individuals in privacy-constrained public health interventions that simultaneously offers provable theoretical guarantees and empirical efficacy.

4 citationsRead paper

Safe Language Generation in the Limit

Jan 13, 2026

This work investigates the theoretical foundations of safe language generation within the framework of extreme learning. It formally defines, for the first time, the tasks of safe language recognition and generation to address the challenge of avoiding harmful or policy-violating content, leveraging formal language theory and computability analysis to assess their feasibility. The study establishes that safe language recognition is generally undecidable and demonstrates that the computational complexity of safe generation is at least as hard as that of conventional language recognition. Furthermore, it delineates the boundary between tractable and intractable cases, precisely characterizing conditions under which safe generation is feasible or impossible. These results establish fundamental theoretical limits and provide a rigorous formal basis for future research on safe language generation.

2 citationsRead paper

A New Look at Bayesian Testing

Feb 11, 2026

This study addresses the theoretical gap between classical hypothesis testing with fixed significance levels and Bayesian methods, particularly in light of the Lindley paradox. By leveraging moderate deviation theory, the authors develop a unified Bayesian framework for hypothesis testing. Through Bayesian risk analysis and asymptotic expansions, they show that the optimal test threshold operates on the scale of √(log n / n), naturally yielding Jeffreys’ threshold, the BIC penalty term, and the Chernoff–Stein error exponent. This framework not only resolves the Lindley paradox but also extends Rubin’s (1965) program to modern settings such as high-dimensional sparse inference, goodness-of-fit testing, and model selection. Moreover, it establishes the superiority of Bayesian procedures over classical Neyman–Pearson tests in terms of statistical risk.

1 citationsRead paper

E-values for Adaptive Clinical Trials: Anytime-Valid Monitoring in Practice

Feb 06, 2026

This work proposes a flexible and rigorous monitoring framework for two-arm randomized controlled trials that addresses the challenge of Type I error control in adaptive designs with frequent interim analyses and data-dependent adaptations. Built upon E-values and E-processes, the approach enables valid inference under composite null hypotheses and supports futility monitoring, seamlessly integrating group sequential and Bayesian perspectives. By constructing E-processes via betting martingales and incorporating calibration strategies, multiplicity adjustments, and hybrid design elements, the method guarantees strict Type I error control without requiring pre-specified analysis times. The framework is implemented in the open-source R package evalinger. Numerical experiments demonstrate that, under continuous monitoring, the proposed method not only maintains exact Type I error control but also achieves higher statistical power compared to conventional group sequential approaches.

1 citationsRead paper
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