MARC details
000 -LEADER |
fixed length control field |
03751cam a22004218i 4500 |
001 - CONTROL NUMBER |
control field |
21440656 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
BD-ChCU |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20240324102125.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
200208s2020 enk b 001 0 eng |
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
LC control number |
2019053276 |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9781108486828 |
Qualifying information |
(hardback) |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
Canceled/invalid ISBN |
9781108571401 |
Qualifying information |
(epub) |
040 ## - CATALOGING SOURCE |
Original cataloging agency |
LBSOR/DLC |
Language of cataloging |
eng |
Description conventions |
rda |
Transcribing agency |
DLC |
042 ## - AUTHENTICATION CODE |
Authentication code |
pcc |
050 00 - LIBRARY OF CONGRESS CALL NUMBER |
Classification number |
QA402.5 |
Item number |
.L367 2020 |
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
519.3 L351 b |
Edition number |
23 |
100 1# - MAIN ENTRY--PERSONAL NAME |
Personal name |
Lattimore, Tor, |
Dates associated with a name |
1987- |
Relator term |
author. |
245 10 - TITLE STATEMENT |
Title |
Bandit algorithms / |
Statement of responsibility, etc. |
Tor Lattimore and Csaba Szepesvari. |
263 ## - PROJECTED PUBLICATION DATE |
Projected publication date |
2005 |
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
Place of production, publication, distribution, manufacture |
Cambridge ; |
-- |
New York, NY : |
Name of producer, publisher, distributor, manufacturer |
Cambridge University Press, |
Date of production, publication, distribution, manufacture, or copyright notice |
2020. |
300 ## - PHYSICAL DESCRIPTION |
Extent |
pages cm |
336 ## - CONTENT TYPE |
Content type term |
text |
Content type code |
txt |
Source |
rdacontent |
337 ## - MEDIA TYPE |
Media type term |
unmediated |
Media type code |
n |
Source |
rdamedia |
338 ## - CARRIER TYPE |
Carrier type term |
volume |
Carrier type code |
nc |
Source |
rdacarrier |
504 ## - BIBLIOGRAPHY, ETC. NOTE |
Bibliography, etc. note |
Includes bibliographical references and index. |
505 0# - FORMATTED CONTENTS NOTE |
Formatted contents note |
Foundations of probability -- Stochastic processes and Markov chains -- Stochastic bandits -- Concentration of measure -- The explore-then-commit algorithm -- The upper confidence bound algorithm -- The upper confidence bound algorithm: asymptotic optimality -- The upper confidence bound algorithm: minimax optimality -- The upper confidence bound algorithm: Bernoulli noise -- The Exp3 algorithm -- The Exp3-IX algorithm -- Lower bounds: basic ideas -- Foundations of information theory -- Minimax lower bounds -- Instance dependent lower bounds -- High probability lower bounds -- Contextual bandits -- Stochastic linear bandits -- Confidence bounds for least squares estimators -- Optimal design for least squares estimators -- Stochastic linear bandits with finitely many arms -- Stochastic linear bandits with sparsity -- Minimax lower bounds for stochastic linear bandits -- Asymptotic lower bounds for stochastic linear bandits -- Foundations of convex analysis -- Exp3 for adversarial linear bandits -- Follow the regularized leader and mirror descent -- The relation between adversarial and stochastic linear bandits -- Combinatorial bandits -- Non-stationary bandits -- Ranking -- Pure exploration -- Foundations of Bayesian learning -- Bayesian bandits -- Thompson sampling -- Partial monitoring -- Markov decision processes. |
520 ## - SUMMARY, ETC. |
Summary, etc. |
"Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes"-- |
Assigning source |
Provided by publisher. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Mathematical optimization. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Probabilities. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Decision making |
General subdivision |
Mathematical models. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Resource allocation |
General subdivision |
Mathematical models. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Algorithms. |
700 1# - ADDED ENTRY--PERSONAL NAME |
Personal name |
Szepesvári, Csaba, |
Relator term |
author. |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Relationship information |
Online version: |
Main entry heading |
Lattimore, Tor, 1987- |
Title |
Bandit algorithms |
Place, publisher, and date of publication |
Cambridge ; New York, NY : Cambridge University Press, 2020 |
International Standard Book Number |
9781108571401 |
Record control number |
(DLC) 2019053277 |
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
a |
7 |
b |
cbc |
c |
orignew |
d |
1 |
e |
ecip |
f |
20 |
g |
y-gencatlg |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
Dewey Decimal Classification |
Koha item type |
Books |
Suppress in OPAC |
No |