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Learning Using Local Membership Queries

Published on Aug 09, 20133560 Views

We introduce a new model of membership query (MQ) learning, where the learning algorithm is restricted to query points that are close to random examples drawn from the underlying distribution. The lea

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Chapter list

Learning using Local Membership Queries00:00
PAC + MQ Learning [Angluin ‘87] - 100:07
PAC + MQ Learning - 101:12
PAC + MQ Learning [Angluin ‘87] - 201:54
PAC + MQ Learning - 202:34
Our Model02:57
PAC + local-MQ Learning03:30
Warmup: 1-local MQs04:23
Our Results(1)05:36
Our Results(2)06:10
Our Results(3)06:30
Learning sparse polynomials - 106:51
Learning sparse polynomials - 206:58
Learning sparse polynomials - 308:01
Learning sparse polynomials - 408:33
Learning sparse polynomials - 509:13
Low degree approximation09:16
Learning sparse polynomials - 610:03
Kushilevitz-Mansour Algorithm - 110:12
Kushilevitz-Mansour Algorithm - 210:38
Kushilevitz-Mansour Algorithm - 311:26
Kushilevitz-Mansour Algorithm - 411:37
Kushilevitz-Mansour Algorithm - 511:59
Our Algorithm - 112:38
Our Algorithm - 213:32
Our Algorithm - 313:38
Our Algorithm - 413:46
Our Algorithm - 514:52
Non-zero-test (x1) - 115:26
Non-zero-test (x1) - 215:48
Non-zero-test (S)16:23
Our Algorithm - 616:35
Our Algorithm - 717:11
Our Algorithm - 817:30
Conclusions17:34
Open Problems18:00
Thank You18:48