We invite contributions spanning monitoring, adaptation, fairness, and governance of ML systems under distributional drift, including (but not limited to):
Drift detection and characterization: Statistical tests, kernel methods, density-ratio estimation, causal or graph-based change detection, sequential monitoring, and early-warning systems for drift identification.
Representation and uncertainty: Calibration, uncertainty estimation, explainability, and representation-based monitoring for identifying concept, covariate, or representation drift.
Adaptation and recovery: Test-time adaptation, online learning, continual learning, domain generalization, and meta-learning approaches for handling evolving data distributions.
Fairness and bias under drift: Methods for maintaining fairness, subgroup robustness, and demographic parity as data or population shifts occur; detection and mitigation of emerging biases during deployment.
Governance and auditing: Frameworks for model monitoring under regulatory and compliance constraints; accountability, audit trails, and explainable intervention mechanisms in high-stakes domains.
Reliability at scale: Systems and infrastructure for large-scale monitoring, data logging, alerting, and automated response in production pipelines.:
Submissions are non-archival and may include under-review work, exploratory or in-progress research, extensions of prior publications, as well as novel and unpublished contributions.
Important dates:
Paper submission opens: January 1, 2026
Paper submission deadline: Feb 10, 2026, 11:59 PM AOE
Review deadline for reviewers: February 25, 2026, 11:59 PM AOE
Author notification: March 1, 2026
Camera-ready deadline for accepted papers: March 16, 2026
Workshop day: April 26 or 27, 2026
Camera-ready submissions are required to follow the formatting guidelines outlined here.
Submissions must be made through OpenReview and formatted using the ICLR conference proceedings style.
We invite submissions to two tracks:
Papers submitted to the main track may be up to 8 pages, excluding references, the appendix, and supplementary materials.
Tiny papers are intended for concise contributions and must be no longer than 4 pages, excluding references, the appendix, and supplementary materials.
All submissions will undergo a double-blind, non-archival review process.
All accepted papers will be presented in an extended poster session at the workshop. In addition, a small number of papers will be selected for spotlight oral presentations.
Beyond the main track, we will host a dedicated track on Lessons from Failures: Understanding What Did Not Work and Why.
Submissions to this track are limited to a maximum of 4 pages, excluding references, and should follow the ICLR formatting guidelines.
This track welcomes submissions that report:
Methods that fell short of expectations
Failed or inconclusive experiments
Unexpected challenges or negative results
By encouraging openness about what did not work, this track aims to reduce duplicated effort, strengthen scientific rigor, and accelerate progress across the community.
To promote inclusivity and broaden participation, we are offering a number of travel grants to support attendees from diverse backgrounds. This initiative is made possible through the generous support of RBC Borealis and aims to foster richer and more inclusive discussions at the workshop.
We are pleased to introduce a Best Paper Award and 3 Best poster awards. to recognize exceptional submissions. Award recipients will receive:
A certificate of recognition
A monetary prize, generously sponsored by RBC Borealis.
Selections will be made based on recommendations from the program committee.