Announcement for Special Issue: Machine Learning and Optimization
Editors: Andre Milzarek, Zaiwen Wen, Junfeng Yang
This special issue aims to bring together articles discussing recent advances in machine learning and optimization. Machine learning, a branch of artificial intelligence, focuses on developing algorithms that allow computers to learn from data and make decisions. Optimization involves finding the best solution from a set of feasible options and is crucial for enhancing machine learning algorithms.
The integration of these two fields offers significant potential for a wide range of applications. Machine learning techniques can improve optimization processes by predicting optimal solutions and enhancing search efficiency. Conversely, optimization methods can refine machine learning models for better accuracy and generalization, leading to breakthroughs in domains like healthcare, finance, and engineering.
We invite contributions that highlight innovative approaches and significant findings in the intersection of machine learning and optimization. Topics of interest include, but are not limited to:
- Optimization algorithms for machine learning
- Machine learning methods for optimization
- Artificial intelligence and optimization
- Applications in various fields such as healthcare, finance, and engineering
- Data-driven optimization techniques
We look forward to your submissions that push the boundaries of research in machine learning and optimization. This special issue aims to provide a platform for researchers to share their latest findings and explore new directions in this exciting and rapidly evolving field.
Important Dates: Deadline for submissions: January 31, 2025.
Submission Procedure: Please submit to the Optimization and Engineering (OPTE) journal at https://www.springer.com/mathematics/journal/11081 and select special issue “SI: Machine Learning and Optimization”. All submissions must be original and may not be under review by another publication. Interested authors should consult the journal’s “Instructions for Authors”, at https://www.springer.com/mathematics/journal/11081. All submitted papers will be reviewed on a peer review basis as soon as they are received. Accepted papers will become immediately available at Online First until the complete Special Issue appears.