Special Issue on High-Dimensional Data Modeling

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 aims to be a collection of frontier research on theory, methodology and application of sparse modeling and high-dimensional change-point and time series analysis.

Submission Deadline: September 30, 2026

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

Topics of Interest

Sparse Modeling

• Sparse neural networks with provable guarantees
• High-dimensional gradients and implicit sparsity
• Sparse PCA, sparse CCA, sparse discriminant analysis
• Nonconvex matrix recovery
• Tensor decomposition with sparsity

Sparse Modeling under Heavy-Tailed or Contaminated Data

• Robust high-dimensional regression
• Sparse PCA with heavy tails
• Huberized and trimmed methods

High-Dimensional Change-Point and Time-Series Analysis

• Sparse VAR models
• Structural breaks in high dimensions
• Online detection with theoretical guarantees

Submission Information

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

Contact: Ruiyan Zhang, zhangry@scichina.com