Sophisticated optimization problems lay in many machine learning tasks. These problems were commonly smartly relaxed as convex optimization problems. Although the relaxation allows an efficient optimization using mathematical programming methods, it often shifts the learning problem and loses some important properties (e.g., convex loss functions may sensitive to data noise).
Evolutionary optimization provides a set of direct search tools that make it possible to solve non-convex optimization problems for machine learning. This special session intends to bring together researchers to report their latest progress and exchange experience in solving machine learning tasks better with evolutionary optimization methods.
The interest of this special session is on solving non-convex optimization problems in machine learning with the methodologies related toevolutionary optimization, such as
- Evolutionary algorithms
- Swarm intelligence algorithms
- Cross-entropy methods
- Bayesian optimization
- Multi-armed badit methods
The topics cover a broad range of machine learning tasks including (but not limited to):
- Supervised, semi-supervised, and multi-label learning
- Learning deep models
- Representation learning, sparse learning, dimension extraction
- Reinforcement learning
- Multi-instance learning
- Cost-sensitive and imbalanced learning
- Unsupervised learning and clustering
- Parameter tuning
Both empirical and theoretical papers are welcome.
Paper should be in U.S. Letter size uisng the templates from IEEE Conference Templates. Up to 8 pages, including figures, tables and references. At maximum, two additional pages are permitted with overlength page charge of US$125/page, to be paid during author registration.
Papers should be submitted via the IEEE WCCI 2016 paper submission site (will be available soon).
When submitting, please do select [CEC-16: Evolutionary Optimization for Non-Convex Machine Learning] in the Main Research topic dropdown list, otherwise the paper will not be reviewed by the invited reviewers of this special session.
More information about paper submission can be found in the WCCI 2016 website.
Nanjing University, China
University of Science and Technology of China, China
|Jose A. Lozano
University of the Basque Country, Spain
We will do our best to ensure that every paper submitted to this special session will be reviewed by at least one expert from evolutionary computation and one expert from machine learning.
(The PC list is to be available soon)