Northwestern Engineering researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials — the guiding rules describing how atoms interact — in ...

OLA2024 welcomes presentations that cover any aspects of optimization and learning research such as new high-impact applications, parameter tuning, 4th industrial revolution, new research challenges, ...

“Machine learning is normally very data intensive, and it’s difficult to generate a lot of data when you’re using high-quality data from finite element analysis. But the multi-objective Bayesian ...

In two, we recognized that many machine learning problems involve minimizing empirical risk functions with well-behaved population risks. Instead of analyzing the non-convex empirical risk directly, ...

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text ...

Zhishen Huang Department of Applied Mathematics, University of Colorado Boulder Finding local minimizers in nonconvex and non-smooth optimization We consider the problem of finding local minimizers in ...