Call for Papers

We are soliciting short papers (4 pages) and long papers (8 pages). There is no limit on the number of pages for references and acknowledgments. Papers must be formatted according to ACM’s sigconf layout (see here for instructions).

• Efficient model prototyping, calibration, inferencing, and serving
• Privacy and security for AI and ML models
• Explainability, interpretability, and robustness of AI models in production
• Testing, debugging, and monitoring models
• Infrastructure and hardware efficient ML models
• Efficient implementation and best practices ML Ops and LLM Ops
• Data Quality check, model surveillance, model diagnostics, and model re-calibration
• AI observability, AI model evaluation metrics

• Paper Length: Short papers (4 pages) and long papers (8 pages). There is no limit on the number of pages for references and acknowledgments.
• Format: Single PDF file, using the ACM’s sigconf layout. ACM LaTeX templates: [download] use the sigconf template.
• Submission Portal: OpenReview.
• Anonymity: All submissions will be peer-reviewed, and judged on originality, technical correctness, relevance, and quality of presentation in a single-blind review process.
• Archival: The workshop will be non-archival in format, but we intend to share the accepted papers on the workshop web page. We optionally ask the speakers to make their talk slides public too. Papers and slides will be linked to the workshop website when they are available.
• Participation: This is a participatory, in-person event. Accepted Authors are encouraged to present their work and discuss it at the event.

• Submission Deadline: October 13, 2024 AOE
• Notification of Acceptance: October 30, 2024 AOE
• Workshop Date: November 15, 2024 1PM - 5PM EST

• Fabio Casati
• Jayeeta Putatunda
• Anant Gupta
• Ashish Bansal
• Masoud Hashemi