Seminar on Computational Learning and Adaptation



Making Good Inferences with Missing Information and Very Little Computation


Daniel Gray Goldstein
Engineering-Economic Systems & Operations Research
Stanford University



In contrast to those who seek more complicated ways to combine all available information to make optimal inferences, I have investigated what happens when one uses simple heuristics which neither use nor combine all available information and also violate various axioms of reasonableness, such as transitivity or the Archimedian axiom. These heuristics, derived from human decision-making strategies, give surprising results, and suggest some new directions one might take in machine learning. 

Date: Wed., May 6; Time: 4:15-5:30PM; Place: Cordura 100

The goal of this seminar is to increase communication among local researchers with interests in computational approaches to learning and adaptation. If you would like to be added to (or removed from) the mailing list, or if you are interested in giving a talk in the seminar, please send email to iba@isle.org.


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