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Stochastic optimization with non-i.id. noise
Published on Jan 25, 20123977 Views
We study the convergence of a class of stable online algorithms for stochastic convex optimization in settings where we do not receive independent samples from the distribution over which we optimize
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Stochastic optimization with non-i.i.d. noise00:00
Basic setup - 0100:12
Basic setup - 0201:12
Basic setup - 0301:24
Basic setup - 0401:56
Online Convex Optimization - 0102:28
Online Convex Optimization - 0203:40
I.I.D. sampling not always possible04:14
Formal setup - 0105:04
Formal setup - 0206:12
Example1: - 0107:27
Example1: - 0208:09
Example1: - 0308:47
Example 2:09:19
Stable online algorithms10:26
Convergence rate for convex losses - 0112:09
Convergence rate for convex losses - 0213:28
Specialization to particular algorithms14:02
Specialization to geometric mixing15:12
Simulation15:59
Example: - 0116:30
Example: - 0216:34
Example: - 0316:36
Example: - 0416:37
Convergence rate of MIGD16:44
Convergence plot for MIGD17:29
Strongly convex loss functions17:51
Better guarantees for strongly convex losses - 0118:20
Better guarantees for strongly convex losses - 0218:53
Fast rates for linear prediction - 0119:18
Fast rates for linear prediction - 0219:35
Conclusions20:19
Extensions20:57
References21:19