Slides in PDF - of Marcus Hutter

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Marcus Hutter - 64 - Universal Induction & Intelligence Universal Inductive Inference: Summary Universal Solomonoff prediction solves/avoids/meliorates many problems of (Bayesian) induction. We discussed: + general total bounds for generic class, prior, and loss, + i.i.d./universal-specific instantaneous and future bounds, + the D n bound for continuous classes, + indifference/symmetry principles, + the problem of zero p(oste)rior & confirm. of universal hypotheses, + reparametrization and regrouping invariance, + the problem of old evidence and updating, + that M works even in non-computable environments, + how to incorporate prior knowledge, − the prediction of short sequences, − the constant fudges in all results and the U-dependence, − M’s incomputability and crude practical approximations.
Marcus Hutter - 65 - Universal Induction & Intelligence Outlook • Relation to Prediction with Expert Advice • Relation to the Minimal Description Length (MDL) Principle • Generalization to Active/Reinforcement learning (AIXI) Open Problems • Prediction of selected bits • Convergence of M on Martin-Loef random sequences • Better instantaneous bounds for M • Future bounds for Bayes (general ξ)
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Foundations of Universal Induction
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Page 91 and 92: Language Tree (Re)construction base
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Slides in PDF - of Marcus Hutter

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