Catch, Adapt, and Operate: Monitoring ML Models Under Drift

🗓️ April 2026

📍 Rio de Janeiro, Brazil

News

📣 First-round financial assistance decisions are now out! All selected recipients have been notified via email. If any awards are declined, we will reach out to second-round applicants on April 2. Thanks to everyone for your interest and patience!

📣 Decisions Released (Here)

📣 Call for Sponsorships - ICLR 2026 CAO Workshop

We invite industry partners and organizations to sponsor the Catch, Adapt, and Operate (CAO): Monitoring ML Models Under Drift workshop at ICLR 2026 in Rio de Janeiro.

Sponsorship offers visibility with leading ML researchers and practitioners, opportunities for engagement and networking, and a chance to support travel grants for students and early-career researchers.

If you’re interested in supporting the workshop, please contact us at:
📧 caoiclr2026@gmail.com

📣 Paper Submission is now open, submit your work here: link

📣 Submissions are non-archival and may include exploratory or in-progress research, extensions of prior publications, as well as novel and unpublished work.

📣 Thanks to RBC Borealis, the workshop will offer 10 travel grants, 1 Best Paper Award, and 3 Best Poster Awards

📣 We invite researchers and practitioners to self-nominate as reviewers for the workshop. If you are interested, please complete the reviewer self-nomination form: link

About

This workshop addresses methods for ensuring the reliability of machine learning systems under distribution drift. Such drift may occur as temporal, label, concept, domain, or representation shift, each presenting unique challenges for monitoring and adaptation. We organize the program into three complementary themes:

Sensing the Drift: Developing tools to detect when models encounter distributional change, using statistical tests, kernel methods, uncertainty estimation, and representation-based monitoring. This theme focuses on identifying drift early and minimizing false alarms in real-world pipelines.
Responding to Drift: Designing strategies that adapt models once drift is detected, including test-time adaptation, continual and online learning, regularization, and selective prediction. The focus is on maintaining accuracy and stability while avoiding catastrophic forgetting.
Operating at Scale: Extending monitoring and adaptation to large-scale production environments, where heterogeneous data streams, governance requirements, and real-time costs amplify the challenge. This theme emphasizes system design, infrastructure, benchmarks, and protocols for reliable deployment at scale.

Together, these themes establish a unified research agenda for developing robust and trustworthy machine learning systems in dynamic, non-stationary environments.

Accepted papers

We are pleased to announce that decisions have been released.

This year, we received an exceptionally strong set of submissions (126 papers) across all tracks. Thanks to the dedication of over 260 reviewers and area chairs, we were able to provide thoughtful and thorough evaluations and assemble a high-quality program.

We sincerely thank all authors for their submissions and engagement with the workshop theme. The level of interest and technical depth in monitoring, reliability, and robustness under distribution shift has been truly encouraging.

For accepted papers:

Further instructions regarding presentation format and logistics will follow shortly.
Authors interested in financial assistance should monitor upcoming communication.

We look forward to an exciting and impactful workshop in