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The Sample-Computational Tradeo ff
Published on Jan 16, 20133934 Views
When analyzing the error of a learning algorithm, it is common to decompose the error into approximation error (measuring how well the hypothesis class fits the problem) and estimation error (due to
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Chapter list
The Sample-Computational Tradeo ff00:00
Based on joint work with:00:00
Agnostic PAC Learning00:42
Error Decomposition02:09
3-term Error Decomposition (Bottou & Bousquet' 08)03:35
Joint Time-Sample Complexity - 104:50
Joint Time-Sample Complexity - 204:59
Joint Time-Sample Complexity - 305:10
Outline - 106:47
Agnostic learning Preferences - 107:22
Agnostic learning Preferences - 211:28
Sample-Computational Tradeo ff - 114:13
Is this the best we can do?15:03
Sample-Computational Tradeo ff - 215:50
HKS: Proof idea16:40
Outline - 218:33
Learning Margin-Based Halfspaces - 119:08
Learning Margin-Based Halfspaces - 223:25
Proof Idea24:35
Proof Idea (Cont.)25:41
Can we do better ?27:57
Proof ideas29:39
Outline - 333:33
Proof: One Way Permutations35:45
Proof: The Learning Problem - 136:01
Proof: The Learning Problem - 236:02
Proof: The Learning Problem - 336:03
Proof: The Learning Problem - 436:03
Proof of Second Claim - 136:04
Proof of Second Claim - 236:06
Proof of First Claim36:07
Proof of Third Claim - 136:08
Proof of Third Claim - 236:11
Proof of Third Claim - 336:19
Summary36:21