Institution profile

Zhejiang University of Finance and Economics

Academic institutionasia · cn
Official website
Research library7linked papers
Opportunities0open roles
Selected work

Representative Papers

CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecasting

Jan 28, 2026

This work addresses the tension in multivariate time series forecasting between modeling inter-channel dependencies and preserving model flexibility: channel-dependent approaches are prone to overfitting due to sensitivity to channel ordering, while channel-independent models neglect inter-channel relationships. To resolve this, we propose CPiRi, a novel framework that introduces permutation invariance over channels into multivariate time series modeling for the first time. CPiRi employs a spatiotemporal decoupling architecture, a frozen pre-trained temporal encoder, a lightweight spatial relation module, and a channel-shuffling training strategy to adaptively infer channel relationships from data. Grounded in permutation equivariance theory, our approach ensures strong inductive generalization to unseen channel configurations. Experiments demonstrate that CPiRi achieves state-of-the-art performance across multiple benchmarks, exhibits robustness to channel order perturbations, generalizes to full-channel settings using only half the channels during training, and maintains efficiency at scale.

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Red alert: Millions of "homeless" publications in Scopus should be resettled

Aug 18, 2025

Scopus contains millions of “homeless” publications—authored by researchers with complete institutional affiliations yet erroneously labeled “country-undefined”—undermining database reliability and compromising the accuracy and fairness of research evaluation. This study systematically identifies four primary root causes: incomplete address information, failure to recognize national name variants, typographical errors, and deficiencies in address parsing algorithms. Leveraging 124 years of Scopus metadata, we integrate bibliometric analysis, multilingual standardization of country names, and fine-grained data cleaning to classify and quantitatively trace these causes. Our findings yield a reproducible methodological framework for metadata quality enhancement, enabling institutional affiliation calibration, cross-national research performance assessment, and optimization of scholarly infrastructure. The approach advances best practices in bibliographic data curation and supports equitable, evidence-based science policy.

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Conformal P-Value in Multiple-Choice Question Answering Tasks with Provable Risk Control

Aug 07, 2025

Large language models (LLMs) frequently exhibit hallucination and produce unreliable outputs in multiple-choice question answering (MCQA). Method: This paper proposes the first trustworthy reasoning framework for MCQA that jointly integrates statistical significance testing and calibration-preserving prediction. It constructs a response distribution via self-consistent sampling, uses response frequency as a test statistic for p-value computation, and builds a minimum prediction set with theoretically guaranteed miscoverage rate ≤ α via empirical risk control. Contribution/Results: It is the first work to introduce hypothesis testing into LLM uncertainty quantification, ensuring strict calibration of prediction sets; it further proves that prediction set size serves as a valid uncertainty measure. Experiments on MMLU and MMLU-Pro demonstrate precise α-level miscoverage control and monotonic reduction of prediction set size with decreasing α, significantly enhancing reliability and interpretability—especially in high-risk scenarios.

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Recent publications

Latest Papers

CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecasting

Jan 28, 2026

This work addresses the tension in multivariate time series forecasting between modeling inter-channel dependencies and preserving model flexibility: channel-dependent approaches are prone to overfitting due to sensitivity to channel ordering, while channel-independent models neglect inter-channel relationships. To resolve this, we propose CPiRi, a novel framework that introduces permutation invariance over channels into multivariate time series modeling for the first time. CPiRi employs a spatiotemporal decoupling architecture, a frozen pre-trained temporal encoder, a lightweight spatial relation module, and a channel-shuffling training strategy to adaptively infer channel relationships from data. Grounded in permutation equivariance theory, our approach ensures strong inductive generalization to unseen channel configurations. Experiments demonstrate that CPiRi achieves state-of-the-art performance across multiple benchmarks, exhibits robustness to channel order perturbations, generalizes to full-channel settings using only half the channels during training, and maintains efficiency at scale.

0 citationsRead paper

Red alert: Millions of "homeless" publications in Scopus should be resettled

Aug 18, 2025

Scopus contains millions of “homeless” publications—authored by researchers with complete institutional affiliations yet erroneously labeled “country-undefined”—undermining database reliability and compromising the accuracy and fairness of research evaluation. This study systematically identifies four primary root causes: incomplete address information, failure to recognize national name variants, typographical errors, and deficiencies in address parsing algorithms. Leveraging 124 years of Scopus metadata, we integrate bibliometric analysis, multilingual standardization of country names, and fine-grained data cleaning to classify and quantitatively trace these causes. Our findings yield a reproducible methodological framework for metadata quality enhancement, enabling institutional affiliation calibration, cross-national research performance assessment, and optimization of scholarly infrastructure. The approach advances best practices in bibliographic data curation and supports equitable, evidence-based science policy.

0 citationsRead paper

Conformal P-Value in Multiple-Choice Question Answering Tasks with Provable Risk Control

Aug 07, 2025

Large language models (LLMs) frequently exhibit hallucination and produce unreliable outputs in multiple-choice question answering (MCQA). Method: This paper proposes the first trustworthy reasoning framework for MCQA that jointly integrates statistical significance testing and calibration-preserving prediction. It constructs a response distribution via self-consistent sampling, uses response frequency as a test statistic for p-value computation, and builds a minimum prediction set with theoretically guaranteed miscoverage rate ≤ α via empirical risk control. Contribution/Results: It is the first work to introduce hypothesis testing into LLM uncertainty quantification, ensuring strict calibration of prediction sets; it further proves that prediction set size serves as a valid uncertainty measure. Experiments on MMLU and MMLU-Pro demonstrate precise α-level miscoverage control and monotonic reduction of prediction set size with decreasing α, significantly enhancing reliability and interpretability—especially in high-risk scenarios.

0 citationsRead paper