Seminar Details
2022-03-08 (14h) : Welcome Seminar
At Euler building (room a.002)
Organized by Mathematical Engineering
Section 1: Towards tight convergence rates for optimization methods on hypoconvex functions
Speaker :
Rotaru, Teodor
Abstract :
Using the framework of performance estimation (PEP), we established the first tight convergence rates of the gradient method for smooth hypoconvex (or weakly-convex) functions. These functions’ curvature (i.e., maximum Hessian eigenvalue) belongs to the interval [µ, L], where µ is negative and L is positive. With the help of PEP, we obtained mathematical proofs for a large range of step sizes. As a direct application, we recommend the optimal step size that minimizes the convergence rate.
Section 2: Accelerating large-scale Kernel Support Vector Machines
Speaker :
Sofiane Tanji (UCLouvain)
Abstract :
Kernel methods provide an elegant extension to well-known linear statistical learning. Due to their poor scalability in time and memory, they have limited applications in large scale learning. We propose Snacks : a kernel SVM solver which can tackle large-scale datasets. Our approach consists in using an accelerated version of the stochastic subgradient method to solve the primal optimization problem on smaller random subspaces. The computational savings do not lead to any degradation in terms of learning performance and we demonstrate the effectiveness of the proposed algorithm on benchmark datasets.