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Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
Published on Feb 4, 20254263 Views
Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine learning. For strongly convex problems, its convergence rate was kno
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Presentation
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization00:00
Stochastic Convex Optimization 41:01
Strongly Convex Stochastic Optimization - 0112:58:08
Strongly Convex Stochastic Optimization - 0219:03:01
Better Algorithms32:40:28
This Work - 0162:24:23
This Work - 0266:21:36
This Work - 0378:30:39
This Work - 0485:07:34
This Work - 0589:50:27
This Work - 0691:37:10
Smooth F - 0197:36:17
Smooth F - 02102:48:03
Smooth F - 03110:37:44
Non-Smooth F130:55:41
Warm-up140:16:43
Second Example164:31:17
Fixing SGD - 01181:45:23
Fixing SGD - 02190:31:58
Experiments - 01205:45:09
Experiments - 02240:32:04
Conclusions and Open Problems250:04:11