★ Place: M212, Gongguan Campus, NTNU
Optimization for Machine learning: Nonsmooth optimization approaches
Prof. Adil Bagirov
Centre for Smart Analytics, Institute of Innovation, Science and Sustainability,
Federation University Australia
Unsupervised learning, semi-supervised learning, supervised learning, regression analysis and clusterwise regression analysis problems are among most important problems in machine learning. There are various optimization models of these problems. Nonsmooth optimization approaches lead to better models with significantly less decision variables than those based on other optimization approaches. In this talk, we discuss nonsmooth optimization, including nonsmooth difference of convex (DC) optimization, models and methods for solving various machine learning problems. We also compare nonsmooth optimization approaches with those based on other optimization approaches.
On some general problems in the theory of operatorial inclusions
Prof. Adrian Petrusel
Faculty of Mathematics and Computer Science, Babes-Bolyai University
Abstract: TBA
Using Optimization Algorithms to Solve Submodular Optimization Problems
Dr. Duong Thi Kim Huyen
Lecturer of Faculty of Computer Science, PHENIKAA University
This talk provides an overview of Submodular Optimization, highlighting the importance of studying submodular optimization problems and discussing various approaches found in the literature. Additionally, we introduce several recent results from researchers at ORLab, School of Computing, PHENIKAA University, and propose a new promising problem.
