Published on Sun May 02 2021 High Dimensional Decision Making, Upper and Lower Bounds Similar Mon Apr 08 2019 Artificial Intelligence Bounded rational decision-making from elementary computations that
reduce uncertainty In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives. Such processes are generally costly, meaning that the amount of uncertainty that can be reduced is limited. Here, we introduce the notion of elementary computation based on a fundamental principle Wed Apr 01 2015 Machine Learning Signatures of Infinity: Nonergodicity and Resource Scaling in
Prediction, Complexity, and Learning We introduce a simple analysis of the structural complexity ofinite-memory processes. Such processes are familiar from the well-known multi-arm Bandit problem. We contrast our analysis with computation-theoretic and statistical inference approaches. Thu Nov 20 2003 Artificial Intelligence Great Expectations. Part II: Generalized Expected Utility as a Universal
Decision Rule Mon Apr 15 2019 Machine Learning Introduction to Multi-Armed Bandits Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Mon Jan 18 2016 Machine Learning Statistical Mechanics of High-Dimensional Inference High-dimensional inference is a problem in the statistical physics of quenched disorder. Our analysis uncovers fundamental limits on the accuracy of inference in high dimensions. We also reveal that widely cherished inference algorithms like maximum likelihood (ML) and maximum-a posteriori (MAP) cannot achieve these limits. Wed Feb 06 2013 Artificial Intelligence Conditional Utility, Utility Independence, and Utility Networks We introduce a new interpretation of two related notions - conditional utility and utility independence. Unlike the traditional interpretation, the new interpretation renders the notions the direct analogues of theirprobabilistic counterparts. We present the notion of utility networks, which do for utilities what Bayesian networks do for probabilities.