Theoretical Analysis of Deep Learning

Submission Guidelines

Submissions should follow the ICANN 2026 formatting guidelines. For detailed instructions, please refer to the ICANN 2026 Submission Page .

• Paper Format: Full papers may contain up to 12 pages (including references). Short extended abstracts (up to 2 pages, non-archival) are also welcome. Please follow the ICANN 2026 submission guidelines and templates available at ICANN Submission Page, under the “Instructions for Preparing Your Paper” section.
• Submission System: Papers must be submitted via the CMT system under the “Workshops” track: CMT Submission Portal. Please select Theoretical Analysis of Deep Learning session for this workshop.
• Review Process: All submissions will undergo peer review. Accepted papers will be included in the ICANN 2026 Workshop Proceedings.
• Originality: Submissions must be original and not under review or published elsewhere.

Please note that each submission may include up to two supplementary files: one PDF and one ZIP file. These supplementary materials are not guaranteed to be considered during the review process and will not be published.

Anonymization Guidelines

The review process follows a double-blind policy. Authors must ensure that their submissions are properly anonymized.

• Remove author information: Do not include author names or affiliations. Use “Anonymous submission”.
• Avoid self-identification: Do not refer to your own work in the first person. Use third-person citations as for any other paper.
• Keep references intact: Do not remove author names from citations. Do not write “reference removed for blind review”.
• Acknowledgements: Omit acknowledgements (e.g., funding) in the submission version.

Submissions that violate anonymization requirements may be rejected without review.

Email: shuo.huang@iit.it