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 present a characterization of empirical price data from sponsored search auctions. We show that simple models drawing bid values independently from a fixed distribution can be...
Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Ga...
- This work investigates bandwidth learning algorithms in a version of a distributed heterogeneous data dissemination system called the Agile Information Control Environment (AICE)...
Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which...
We have investigated two major issues in Distributed Information Retrieval (DIR), namely: collection selection and search results merging. While most published works on these two ...