Koolen, W.M.

English

Dagelijks baseren we talloze praktische beslissingen en handelingen op adviezen van experts. Helaas zijn de experts het lang niet altijd met elkaar eens. Wouter Koolen ontwierp nieuwe computerprogramma’s die automatisch leren welk advies het beste gevolgd kan worden. Deze computerprogramma’s weten ook precies hoe ze door de tijd moeten schakelen tussen de adviezen van de verschillende deskundigen. Dit onderzoek is onderdeel van online learning, een opkomende discipline op het grensvlak tussen informatica, informatietheorie en statistiek. Koolen beschrijft toepassingen variërend van investeren in aandelen, kansvoorspellen en datacompressie tot routeplanning. In elk van deze gevallen bewijst hij wiskundig dat zijn computerprogramma's net zo succesvol zijn als een hypothetische beslisser met complete voorkennis van de toekomst, minus lage leerkosten

International Migration, Integration and Social Cohesion online publications

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Combining strategies efficiently: high-quality decisions from conflicting adviceKoolen, W.M.Publication date2011Link to publicationCitation for published version (APA):Koolen, W. M. (2011). Combining strategies efficiently: high-quality decisions from conflictingadvice. [Thesis, fully internal, Universiteit van Amsterdam].General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:09 Mar 2023List of FloatsFigures1.1 Hierarchy of expert types . . . . . . . . . . . . . . . . . . . 112.1 Minimax regret of the regret game with time horizon T . . 342.2 Minimax strategy in the regret game with time horizon . . 362.3 Minimax strategy in the regret game with loss horizon . . 452.4 Backwards induction solution to the Ξ-regret game . . . . 492.5 Minimax regret of the regret game with 0–5 switches . . . 522.6 Minimax regret of the regret game with 0–5 switches (fit) 533.1 HMMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.2 Standard Bayesian mixture bayes[Ξ, w] . . . . . . . . . . . 713.3 Fixed elementwise mixture em[Ξ, w] . . . . . . . . . . . . . 723.4 Unfolding example . . . . . . . . . . . . . . . . . . . . . . . 753.5 Interval notation . . . . . . . . . . . . . . . . . . . . . . . . 763.6 Fixed share: fs[Ξ, w, α] . . . . . . . . . . . . . . . . . . . . . 853.7 Interpolation example: graph structure . . . . . . . . . . . 883.8 The switching method interpolator sm . . . . . . . . . . . . 973.9 The run-length model interpolator rl[τ, c] . . . . . . . . . 993.10 Parameter drift: pd[α] . . . . . . . . . . . . . . . . . . . . . 1053.11 Irreflexive switching . . . . . . . . . . . . . . . . . . . . . . 1094.1 Example learning expert . . . . . . . . . . . . . . . . . . . . 1164.2 Full, freezing and sleeping reference schemes . . . . . . . 1184.3 Generalisation relation among prediction strategies . . . . 121xi4.4 Bayesian network specification of an EHMM . . . . . . . . 1264.5 Hidden state transitions in slot machine HMM . . . . . . . 1264.6 Sleeping and Freezing EHMMs . . . . . . . . . . . . . . . . 1284.7 Notation example . . . . . . . . . . . . . . . . . . . . . . . . 1425.1 An example play Λ for Nature . . . . . . . . . . . . . . . . 1475.2 Worst-case plays for Nature are continuous. . . . . . . . . 1575.3 Domain of integration example . . . . . . . . . . . . . . . . 1595.4 Regularisation imposed by the ε-pruning Algorithm 5.1 . 1626.1 Expanded Hedge is not Component Hedge on paths . . . 184Tables2.1 Solution of 2× 2 matrix games . . . . . . . . . . . . . . . . 254.1 Mixing schemes . . . . . . . . . . . . . . . . . . . . . . . . . 1325.1 Example priors. . . . . . . . . . . . . . . . . . . . . . . . . . 1556.1 Example structured concept classes . . . . . . . . . . . . . 173Protocols1.1 Sequential point forecasting . . . . . . . . . . . . . . . . . . 61.2 Sequential probability forecasting . . . . . . . . . . . . . . 8Algorithms2.1 Minimax algorithm for time horizon T . . . . . . . . . . . 372.2 Minimax algorithm for loss horizon k . . . . . . . . . . . . 543.1 Forward algorithm . . . . . . . . . . . . . . . . . . . . . . . 774.1 Evolving past posteriors . . . . . . . . . . . . . . . . . . . . 1315.1 The ε-pruning algorithm . . . . . . . . . . . . . . . . . . . . 161xii

Combining strategies efficiently: high-quality decisions from conflicting advice

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Combining strategies efficiently: high-quality decisions from conflicting advice

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