3 papers
math.OC2025
Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
Artem Agafonov, Vladislav Ryspayev, Samuel Horváth +3
Quasi-Newton methods are widely used for solving convex optimization problems due to their ease of implementation, practical efficiency, and strong local convergence guarantees. Ho…
cs.CV2025
Learning Confident Classifiers in the Presence of Label Noise
Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii +4
The success of Deep Neural Network (DNN) models significantly depends on the quality of provided annotations. In medical image segmentation, for example, having multiple expert ann…
math.OC2024
OPTAMI: Global Superlinear Convergence of High-order Methods
Dmitry Kamzolov, Dmitry Pasechnyuk, Artem Agafonov +2
Second-order methods for convex optimization outperform first-order methods in terms of theoretical iteration convergence, achieving rates up to for highly-smooth funct…