大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning in the Big Data Regime

Chau Ngoc Huy (Osaka University)

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4. Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning in the Big Data Regime

大阪大学 数理・データ科学セミナー データ科学セミナーシリーズ 第48回

Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning in the Big Data Regime

Chau Ngoc Huy (Osaka University)

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) is a momentum version of stochastic gradient descent with properly injected Gaussian noise to find a global minimum. In this paper, non-asymptotic convergence analysis of SGHMC is given in the context of non-convex optimization, where subsampling techniques are used over an i.i.d dataset for gradient updates. Our results complement those of [RRT17] and improve on those of [GGZ18].