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• W2804572916 abstract "In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems. All proposed algorithms can escape from saddle points with at most $O(log d)$ iterations, which is nearly dimension-free. Further, we show that ASGLD and ASG converge to a local minimum with at most $O(log d/epsilon^4)$ iterations. Also, ASGLD with full gradients or ASGLD with a slowly linearly increasing batch size converge to a local minimum with iterations bounded by $O(log d/epsilon^2)$, which outperforms existing first-order methods." @default.
• W2804572916 created "2018-06-01" @default.
• W2804572916 creator A5027724386 @default.
• W2804572916 creator A5057117256 @default.
• W2804572916 date "2018-05-23" @default.
• W2804572916 modified "2023-09-27" @default.
• W2804572916 title "Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time." @default.
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