Welcome to OWRML'26
Modern machine learning systems are predominantly trained and evaluated under closed-world assumptions: i.i.d. data distributions, fixed label spaces, and abundant clean annotations.
Real-world deployments, however, operate in open, dynamic, and resource-constrained environments where models must handle label noise, distribution shifts, unseen concepts, and evolving tasks. OWRML'26 focuses on principled algorithms for robust learning in open-world scenarios, bridging theoretical guarantees with practical deployment needs.
The workshop aligns with PRICAI 2026's emphasis on trustworthy, generalizable, and deployment-ready AI. It aims to foster cross-disciplinary dialogue on how to build models that remain reliable when the world refuses to stay closed.
Important Dates
- Paper Submission DeadlineTo be announced
- Acceptance NotificationTo be announced
- Camera-ready DeadlineTo be announced
- Workshop DateTo be announced
Objectives and Scope
Unify Research Streams
Bring together fragmented research on robust learning under supervision scarcity, distribution shift, and environmental dynamics.
Showcase New Algorithms
Highlight methods that jointly optimize robustness, adaptability, and data efficiency in open-world environments.
Bridge Theory and Practice
Connect theoretical guarantees with real-world case studies, industry challenges, and open research roadmaps.
Call for Papers
We invite submissions on robust, adaptive, and trustworthy machine learning methods for open-world environments. Topics include, but are not limited to, the following areas.
Learning from Imperfect Supervision
- Weakly supervised learning
- Noisy-label, partial-label, and complementary-label learning
- Crowdsourcing, active learning, and semi-supervised learning
Robustness to Distribution Shift and Openness
- Out-of-distribution learning and open-set recognition
- Domain generalization and domain adaptation
- Novel class discovery and test-time adaptation
Data-Efficient and Lifelong Adaptation
- Continual and lifelong learning
- Few-shot and zero-shot learning
- Concept drift and resource-constrained adaptation
Trustworthy Open-World AI
- Fairness, calibration, and uncertainty quantification
- Theoretical convergence and identifiability guarantees
- Scalable implementations and deployment-ready evaluation
Format and Tentative Program
OWRML'26 is planned as a half-day workshop with invited talks, contributed paper presentations, networking, and a panel discussion.
| Time | Activity |
|---|---|
| 08:30–08:45 | Opening remarks and scope introduction |
| 08:45–09:30 | Invited keynote: Robust Representation and Adaptation in Open Environments (TBA) |
| 09:30–10:15 | Contributed Paper Session I, 2–3 talks with Q&A |
| 10:15–10:30 | Coffee break and networking |
| 10:30–11:45 | Contributed Paper Session II, 3–4 talks with Q&A |
| 11:45–12:20 | Panel and open discussion: Benchmarks, Deployment Pitfalls, and Standardizing Open-World Evaluation |
| 12:20–12:30 | Closing remarks |
Audience participation will be encouraged through Q&A after each session, live polls during panel discussions, and a dedicated Slack or Discord channel for continuous interaction throughout the workshop.
Paper Submission
Papers should be submitted through EasyChair. The submission link will be announced soon.
- Submission system: EasyChair, TBA
- Review process: Dual-anonymized peer review
- Review standard: At least two reviewers per paper
- Acceptance criteria: Relevance, technical quality, novelty, clarity, and potential impact
Target Audience
The workshop targets researchers in machine learning and deep learning, AI engineers deploying models in dynamic or noisy domains such as autonomous systems, healthcare, finance, NLP, and computer vision, as well as PhD students and postdoctoral scholars interested in robust and adaptive AI.
Organizing Committee
Shao-Yuan Li specializes in robust machine learning for open environments. Her research focuses on weakly supervised learning, domain adaptation, and continual learning. She has published 40+ papers in top venues including ICML, NeurIPS, CVPR, AAAI, and IEEE TKDE, with 900+ citations and an h-index of 12. She received the PRICAI 2018 Best Paper Award and the 2023 Jiangsu Science & Technology Award.
Chuanxing Geng's research focuses on open-set recognition, zero-shot learning, and contrastive representation. He is the lead author of a highly cited IEEE TPAMI survey on open-set recognition, with 1,600+ citations. His publications appear in TPAMI, TKDE, AAAI, and NeurIPS. He has an h-index of 11 and received the Jiangsu Outstanding Doctoral Dissertation Award in 2022 and Jiangsu Natural Science Award in 2024.
Sheng-Jun Huang is a leading expert in active learning and robust optimization. He has published 140+ papers with 6,900+ citations and an h-index of 34 in IEEE TPAMI, TKDE, AAAI, and NeurIPS. He co-authored a seminal TPAMI survey on open-set recognition and pioneered query-efficient active learning and robust AUC maximization. He serves on program committees for IJCAI, KDD, AAAI, and PRICAI, and regularly co-organizes international workshops.
Attendance and Promotion
Expected Participants
60–120 attendees.
Expected Submissions
30–40 submitted papers.
Expected Accepted Papers
6–8 accepted papers.
Promotion Strategy
- Dissemination through the official PRICAI 2026 website and newsletter.
- Targeted outreach via ML/DL mailing lists, OpenReview, Reddit r/MachineLearning, WeChat academic communities, and LinkedIn groups.
- Early-bird call for papers with clear submission guidelines.
- Transparent dual-anonymized peer review and acceptance criteria.
Contact
Shao-Yuan Li
Nanjing University of Aeronautics and Astronautics
Email: lisy@nuaa.edu.cn