Robert H. Sloan – författare
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Online surveillance of our behavior by private companies is on the increase, particularly through the Internet of Things and the increasing use of algorithmic decision-making. This troubling trend undermines privacy and increasingly threatens our ability to control how information about us is shared and used. Written by a computer scientist and a legal scholar, The Privacy Fix proposes a set of evidence-based, practical solutions that will help solve this problem. Requiring no technical or legal expertise, the book explains complicated concepts in clear, straightforward language. Bridging the gap between computer scientists, economists, lawyers, and public policy makers, this book provides theoretically and practically sound public policy guidance about how to preserve privacy in the onslaught of surveillance. It emphasizes the need to make tradeoffs among the complex concerns that arise, and it outlines a practical norm-creation process to do so.
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Online surveillance of our behavior by private companies is on the increase, particularly through the Internet of Things and the increasing use of algorithmic decision-making. This troubling trend undermines privacy and increasingly threatens our ability to control how information about us is shared and used. Written by a computer scientist and a legal scholar, The Privacy Fix proposes a set of evidence-based, practical solutions that will help solve this problem. Requiring no technical or legal expertise, the book explains complicated concepts in clear, straightforward language. Bridging the gap between computer scientists, economists, lawyers, and public policy makers, this book provides theoretically and practically sound public policy guidance about how to preserve privacy in the onslaught of surveillance. It emphasizes the need to make tradeoffs among the complex concerns that arise, and it outlines a practical norm-creation process to do so.
Computational Learning Theory
15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings
Häftad, Engelska, 2002
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ThisvolumecontainspaperspresentedattheFifteenthAnnualConferenceon ComputationalLearningTheory(COLT2002)heldonthemaincampusofthe UniversityofNewSouthWalesinSydney,AustraliafromJuly8to10,2002. Naturally,thesearepapersinthe?eldofcomputationallearningtheory,a- search?elddevotedtostudyingthedesignandanalysisofalgorithmsformaking predictionsaboutthefuturebasedonpastexperiences,withanemphasisonr- orousmathematicalanalysis. COLT2002wasco-locatedwiththeNineteenthInternationalConferenceon MachineLearning(ICML2002)andwiththeTwelfthInternationalConference onInductiveLogicProgramming(ILP2002). NotethatCOLT2002wasthe?rstconferencetotakeplaceafterthefull mergeroftheAnnualConferenceonComputationalLearningTheorywiththe EuropeanConferenceonComputationalLearningTheory. (In2001ajointc- ferenceconsistingofthe5thEuropeanConferenceonComputationalLearning Theoryandthe14thAnnualConferenceonComputationalLearningTheory washeld;thelastindependentEuropeanConferenceonComputationalLea- ingTheorywasheldin1999. ) ThetechnicalprogramofCOLT2002contained26papersselectedfrom 55submissions.Inaddition,ChristosPapadimitriou(UniversityofCaliforniaat Berkeley)wasinvitedtogiveakeynotelectureandtocontributeanabstractof hislecturetotheseproceedings. TheMarkFulkAwardispresentedannuallyforthebestpapercoauthored byastudent. Thisyear'sawardwaswonbySandraZillesforthepaper"Merging UniformInductiveLearners. " April2002 JyrkiKivinen RobertH. Sloan Thanks and Acknowledgments Wegratefullythankalltheindividualsandorganizationsresponsibleforthe successoftheconference. ProgramCommittee Weespeciallywanttothanktheprogramcommittee:DanaAngluin(Yale), JavedAslam(Dartmouth),PeterBartlett(BIOwulfTechnologies),ShaiBen- David(Technion),JohnCase(Univ. ofDelaware),PeterGru..nwald(CWI),Ralf Herbrich(MicrosoftResearch),MarkHerbster(UniversityCollegeLondon), G'aborLugosi(PompeuFabraUniversity),RonMeir(Technion),ShaharMend- son(AustralianNationalUniv. ),MichaelSchmitt(Ruhr-Universit..atBochum), RoccoServedio(Harvard),andSantoshVempala(MIT). WealsoacknowledgethecreatorsoftheCyberChairsoftwareformakinga softwarepackagethathelpedthecommitteedoitswork. Local Arrangements, Co-located Conferences Support SpecialthanksgotoourconferencechairArunSharmaandlocalarrangements chairEricMartin(bothatUniv. ofNewSouthWales)forsettingupCOLT2002 inSydney.RochelleMcDonaldandSueLewisprovidedadministrativesupport. ClaudeSammutinhisroleasconferencechairofICMLandprogramco-chair ofILPensuredsmoothcoordinationwiththetwoco-locatedconferences. COLT Community ForkeepingtheCOLTseriesgoing,wethanktheCOLTsteeringcommittee, andespeciallyChairJohnShawe-TaylorandTreasurerJohnCaseforalltheir hardwork. WealsothankStephenKwekformaintainingtheCOLTwebsiteat http://www. learningtheory. org. Sponsoring Institution SchoolofComputerScienceandEngineering,UniversityofNewSouthWales, Australia VIII Thanks and Acknowledgments Referees PeterAuer LisaHellerstein AlainPajor AndrewBarto DanielHerrmann GunnarR..atsch StephaneBoucheron ColindelaHiguera RobertSchapire OlivierBousquet SeanHolden JohnShawe-Taylor Nicol'oCesa-Bianchi MarcusHutter TakeshiShinohara TapioElomaa SanjayJain DavidShmoys RanEl-Yaniv YuriKalnishkan YoramSinger AllanErskine MakotoKanazawa CarlSmith HenningFernau SatoshiKobayashi FrankStephan J..urgenForster VladimirKoltchinskii Gy..orgyTur'an DeanFoster MattiKa...ariai ..nen PaulVitan 'yi ClaudioGentile WeeSunLee ManfredWarmuth JudyGoldsmith ShieMannor JonA. Wellner ThoreGraepel RyanO'Donnell RobertC.Williamson Table of Contents Statistical Learning Theory AgnosticLearningNonconvexFunctionClasses...1 Shahar Mendelson andRobertC. Williamson Entropy,CombinatorialDimensionsandRandomAverages...14 Shahar Mendelson andRoman Vershynin GeometricParametersofKernelMachines...29 Shahar Mendelson LocalizedRademacherComplexities...44 PeterL. Bartlett,Olivier Bousquet,and Shahar Mendelson SomeLocalMeasuresofComplexityofConvexHulls andGeneralizationBounds ...59 Olivier Bousquet,Vladimir Koltchinskii, and DmitriyPanchenko OnlineLearning PathKernelsandMultiplicativeUpdates...74 Eiji Takimoto andManfred K. Warmuth PredictiveComplexityandInformation...90 Michael V. Vyugin andVladimir V. V'yugin MixabilityandtheExistenceofWeakComplexities...105 YuriKalnishkan andMichael V. Vyugin ASecond-OrderPerceptronAlgorithm...121 Nicolo ' Cesa-Bianchi, AlexConconi, and Claudio Gentile TrackingLinear-ThresholdConceptswithWinnow ...138 Chris Mesterharm Inductive Inference LearningTreeLanguagesfromText...153 HenningFernau PolynomialTimeInductiveInferenceofOrderedTreePatterns withInternalStructuredVariablesfromPositiveData ...169 YusukeSuzuki,RyutaAkanuma,Takayoshi Shoudai, TetsuhiroMiyahara, andTomoyuki Uchida X Table of Contents InferringDeterministicLinearLanguages...185 Colin dela HigueraandJoseOncina MergingUniformInductiveLearners...201 SandraZilles TheSpeedPrior:ANewSimplicityMeasure YieldingNear-OptimalComputablePredictions...216 J.. urgenSchmidhuber PAC Learning NewLowerBoundsforStatisticalQueryLearning...229 KeYang ExploringLearnabilitybetweenExactandPAC...244 Nader H. Bshouty, Je?reyC. Jackson, andChristino Tamon PACBoundsforMulti-armedBanditandMarkovDecisionProcesses...