Overview
The Workshop on Constrained Optimization for Machine Learning (COML) at NeurIPS 2025 has announced an extension for the submission deadline of extended abstracts. This workshop aims to bring together researchers and practitioners interested in the intersection of constrained optimization and machine learning, highlighting its significance in developing robust and trustworthy AI systems.
Background & Relevance
Constrained optimization is a crucial area within machine learning that focuses on optimizing models while adhering to specific constraints. This field is particularly relevant as it addresses challenges in ensuring that AI systems are not only effective but also safe and fair. The integration of constrained optimization methods can lead to advancements in various applications, including deep learning, where constraints can enforce desirable properties such as interpretability and robustness.
Key Details
- Event: Workshop on Constrained Optimization for Machine Learning (COML)
- Conference: NeurIPS 2025
- Extended Submission Deadline: Thursday, August 28, 2025 (AOE)
- Website: COML Workshop
- Abstract Length: 4 pages
Eligibility & Participation
This workshop invites contributions from researchers, practitioners, and students who are engaged in the study and application of constrained optimization methods in machine learning. It is an excellent opportunity for those looking to share their findings and connect with others in the field.
Submission or Application Guidelines
To participate, authors are encouraged to submit extended abstracts that cover topics related to constrained optimization in machine learning. Submissions should be formatted according to the workshop guidelines and submitted through the provided website. The focus areas include:
– Fundamental algorithms for constrained optimization, particularly for deep learning problems.
– Statistical learning theory addressing constrained problems.
– Learning-to-optimize strategies for constrained learning tasks.
– Practical applications of constrained optimization in ensuring AI safety and trustworthiness.
– Development of software tools that facilitate constrained deep learning workflows.
More Information
The workshop is set to take place in San Diego, providing a platform for discussions on the latest advancements in constrained optimization. The relevance of this workshop extends beyond theoretical contributions, as it aims to foster practical applications that can enhance the reliability of AI systems in real-world scenarios.
Conclusion
Researchers and practitioners are encouraged to take advantage of this extended deadline to submit their work to the COML workshop at NeurIPS 2025. This event represents a significant opportunity to engage with cutting-edge research and to contribute to the ongoing dialogue around constrained optimization in machine learning. Explore the topics, prepare your submissions, and join the community in San Diego!
Category: CFP & Deadlines
Tags: neurips, machine learning, constrained optimization, deep learning, statistical learning, fairness, robustness, interpretability, AI safety, optimization methods
