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Beyond Stochastic Gradient Descent
Published on Aug 26, 20137146 Views
Many machine learning and signal processing problems are traditionally cast as convex optimization problems. A common difficulty in solving these problems is the size of the data, where there are man
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Chapter list
Beyond stochastic gradient descent00:00
Context00:29
Outline (1)03:04
Supervised machine learning04:25
Smoothness and strong convexity (1)06:39
Smoothness and strong convexity (2)07:28
Smoothness and strong convexity (3)07:40
Smoothness and strong convexity (4)08:41
Iterative methods for minimizing smooth functions10:14
Stochastic approximation12:24
Convex stochastic approximation14:28
Convex stochastic approximation-existing work (1)15:55
Convex stochastic approximation-existing work (2)17:23
Adaptive algorithm for logistic regression (1)19:05
Adaptive algorithm for logistic regression (2)20:16
Least-mean-square algorithm23:02
Markov chain interpretation of constant step sizes25:08
Simulations - synthetic examples (1)26:59
Simulations - benchmarks (1)28:49
Beyond last-squares-Markov chain interpretation30:17
Simulations - synthetic examples (2)31:38
Restoring convergence through online Newton steps33:02
Choice of support point for online Newton steps35:23
Simulations - synthetic examples (3)37:17
Simulations - benchmarks (2)38:21
Outline (2)39:14
Going beyond a single pass over the data39:37
Stochastic vs. deterministic methods (1)41:06
Stochastic vs. deterministic methods (2)41:15
Stochastic vs. deterministic methods (3)41:18
Stochastic vs. deterministic methods (4)41:44
Stochastic average gradient (1)41:52
Stochastic average gradient (2)43:51
Conclusions44:14