Presented by:
Michael Jordan University of California, Berkeley
Date:
Monday 15th January 2018 - 10:00 to 10:45
Venue:
INI Seminar Room 1
Abstract:
Many new theoretical challenges have arisen in the area of gradient-based
optimization for large-scale statistical data analysis, driven by the needs of
applications and the opportunities provided by new hardware and software platforms.
I discuss several related, recent results in this area: (1) a new framework
for understanding Nesterov acceleration, obtained by taking a continuous-time,
Lagrangian/Hamiltonian/symplectic perspective, (2) a discussion of how to
escape saddle points efficiently in nonconvex optimization, and (3) the
acceleration of Langevin diffusion.
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