Monte-Carlo Tree Search and Billiards - VideoLectures

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Some resultsPassive learningSetting:- Linear sep. of R D- Dimension : 4, 8- # queries: 15, 20X-axis: log(# sims)Y-axis: Gen. ErrorAlmost optimal AL(QbC-based)D=4, # =15D=8, # =20

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Some resultsPassive learningSetting:- Linear sep. of R D- Dimension : 4, 8- # queries: 15, 20X-axis: log(# sims)Y-axis: Gen. ErrorAlmost optimal AL(QbC-based)D=4, # =15D=8, # =20

Some results - 2Combining with ALcriteria (inspired fromQbC)Best of both worlds!D=4, # =15Almost optimal AL(QbC-based)D=8, # =20

Page 1 and 2: European Conference in Machine Lear
Page 3 and 4: A major Machine Learning goalReduce
Page 5 and 6: How AL typically worksFind a way to
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: Baal: draw surrogate hypothesesBill
Page 17: Thanks for listening

Monte-Carlo Tree Search and Billiards - VideoLectures

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