We apply a novel theoretical approach to better understand the behaviour of different types of bare-bones PSOs. It avoids many common but unrealistic assumptions often used in an...
This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
We introduce a new model for learning with membership queries in which queries near the boundary of a target concept may receive incorrect or “don’t care” responses. In part...
Avrim Blum, Prasad Chalasani, Sally A. Goldman, Do...
: In the distribution-free property testing model, the distance between functions is measured with respect to an arbitrary and unknown probability distribution D over the input dom...
In implementations of non-standard database systems, large objects are often embedded within an aggregate of different types, i.e. a tuple. For a given size and access probabilit...