Coding Theory for Large-Scale Machine Learning

Call for Papers

Scope of the Workshop

In this workshop we solicit research papers focused on the application of coding and information-theoretic techniques for distributed machine learning. More broadly, we seek papers that address the problem of making machine learning more scalable, efficient, and robust. Both theoretical as well as experimental contributions are welcome. We invite authors to submit papers on topics including but not limited to:

• Asynchronous Distributed Training Methods
• Communication-Efficient Training
• Model Compression and Quantization
• Gradient Coding, Compression and Quantization
• Erasure Coding Techniques for Straggler Mitigation
• Data Compression in Large-scale Machine Learning
• Erasure Coding Techniques for ML Hardware Acceleration
• Fast, Efficient and Scalable Inference
• Secure and Private Machine Learning
• Data Storage/Access for Machine Learning Jobs
• Performance evaluation of coding techniques

Accepted contributions will be presented as posters during the workshop.

Key Dates

Paper Submission: May 3rd, 2019, 11:59 PM anywhere on earth May 6th, 11:59 PM Pacific time.

Decision Notification: May 13th, 2019.

Workshop date: June 14 or 15, 2019

Submission Format and Instructions

The authors should prepare extended abstracts in the ICML paper format and submit via CMT. Submitted papers may not exceed three (3) single-spaced double-column pages excluding references. All results, proofs, figures, tables must be included in the 3 pages. The submitted manuscripts should include author names and affiliations, and an abstract that does not exceed 250 words. The authors may include a link to an extended version of the paper that includes supplementary material (proofs, experimental details, etc.) but the reviewers are not required to read the extended version.

Dual Submission Policy

Accepted submissions will be considered non-archival and can be submitted elsewhere without modification, as long as the other venue allows it. Moreover, submissions to CodML based on work recently accepted to other venues are also acceptable (though authors should explicitly make note of this in their submissions).