Full revelation of private values is impractical in many large-scale markets, where posted price mechanisms are a simpler alternative. In this work, we compare the asymptotic beha...
We investigate algebraic, logical, and geometric properties of concepts recognized by various classes of probabilistic classifiers. For this we introduce a natural hierarchy of pr...
We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means that there exists a learning problem such that fo...
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure ...
Charles Kemp, Thomas L. Griffiths, Sean Stromsten,...
In this paper, we show how our AI opponents learn internal representations of probabilities. We use a Bayesian interpretation of such subjectivist probabilities but do not impleme...