—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
In this paper, we develop and evaluate several probabilistic models of user click-through behavior that are appropriate for modeling the click-through rates of items that are pres...
Hila Becker, Christopher Meek, David Maxwell Chick...
In this paper we present lessons learned in the Evaluating Predictive Uncertainty Challenge. We describe the methods we used in regression challenges, including our winning method ...
Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield over...
Elena Demidova, Peter Fankhauser, Xuan Zhou, Wolfg...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...