【专辑征稿】《统计学习与数据科学（英文）》“统计与人工智能”专辑

以下文章来源于统计学习与数据科学，作者张睿燕

Call for Papers

Special Issue on Statistics and AI

SLADS

Statistical Learning and Data Science

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Aims and Scope

Statistical Learning and Data Science (SLADS) is a newly launched journal sponsored by the Chinese Academy of Sciences, dedicated to publishing high-quality research across statistics, machine learning, artificial intelligence, and data science. SLADS emphasizes both rapid publication and rigorous peer review, using the OpenReview system to ensure transparency and quality. The editorial goal is to reach an Accept/Reject decision within 3.5 months of submission while maintaining high scholarly standards.

This special issue on Statistics and AI aims to offer a venue for publishing high-impact statistical work in the theory, methodology, and applications at the frontier of AI. We seek to highlight research that either (1) applies statistical methodology to understand, improve, and validate AI systems, or (2) develops novel AI-driven approaches to solve complex statistical and data science problems. We are particularly interested in submissions that bridge the gap between theory and practice and address the reliability and efficiency of AI from a statistical perspective.

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Guest Editors

Xiaowu Dai (University of California, Los Angeles)

Linglong Kong (University of Alberta)

Weijie Su (University of Pennsylvania)

Zhihua Zhang (Peking University)

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Submission Deadline: March 31, 2026

Early submissions will be reviewed and published online ahead of the final issue.

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Topics of Interest

1. Foundations of Trustworthy AI

Statistical methods for AI alignment, interpretability, fairness, privacy, and watermarking.

Uncertainty quantification, calibration, robustness, evaluation, and "physics" of AI.

Statistical challenges in data-centric AI, including data mixture, attribution, synthetic data, and copyright.

2. Innovations in Statistical Learning

Statistical approaches to generative modeling (e.g., diffusion models, GANs, VAEs).

Advances in reinforcement learning with statistical guarantees.

Methodologies for self-supervised, semi-supervised, and unsupervised learning.

3. AI for Statistics and Science

AI-driven methods for high-dimensional data analysis and scientific discovery.

Integration of classical statistical models (e.g., time series, spatio-temporal) with deep learning.

Simulation-based inference.

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Submission Information

Manuscripts should be submitted through the SLADS website at http://slads.scichina.com.

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Contact

Ruiyan Zhang, zhangry@scichina.com

Join us in shaping a timely and influential special issue at the interface of statistics and AI!