Mimar Sinan Fine Arts University Mathematics Department Seminars

Parallel Incremental Optimization Algorithm for Solving Partially Separable Problems in Machine Learning
İlker Birbil
Sabancı University, Turkey
Özet : Consider a recommendation problem, where multiple firms are willing to cooperate to improve their rating predictions. However, the firms insists on finding a machine learning approach, which guarantees that their data remain in their own servers. To solve this problem, I will introduce our recently proposed approach HAMSI (Hessian Approximated Multiple Subsets Iteration). HAMSI is a provably convergent, second order incremental algorithm for solving large-scale partially separable optimization problems. The algorithm is based on a local quadratic approximation, and hence, allows incorporating curvature information to speed-up the convergence. HAMSI is inherently parallel and it scales nicely with the number of processors. I will conclude my talk with several implementation details and our numerical results on a set of matrix factorization problems.
  Tarih : 26.10.2017
  Saat : 16:00
  Yer : Seminar room, Bomonti Campus
  Dil : English
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