Monte-Carlo Tree Search and Billiards - VideoLectures

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How AL typically worksFind a way to measure ”information” brought byinstancesGreedily choose the most informative instancesExamples:version space split,Query-by-Committee(classification)Seung et al. 97=> Good, but notoptimal

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That's what Active Learning does!Learning a thresholdPASSIVE:ACTIVE:Exponential improvementFreund et. Al. 97,Dasgupta 04,05, ...This can be generalized (cf <strong>Billiards</strong> later on)

Page 1 and 2: European Conference in Machine Lear
Page 3: A major Machine Learning goalReduce
Page 7 and 8: Train the Active Learning PlayerIns
Page 9 and 10: UCT: Exploration Vs ExploitationUCB
Page 11 and 12: The Baal AlgorithmValue function fo
Page 13 and 14: Baal: draw surrogate hypothesesBill
Page 15 and 16: Some results - 2Combining with ALcr
Page 17: Thanks for listening

Monte-Carlo Tree Search and Billiards - VideoLectures

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