We study the problem of context-sensitive ranking for document retrieval, where a context is defined as a sub-collection of documents, and is specified by queries provided by do...
In this paper we present a family of algorithms for estimating stream weights for dynamic Bayesian networks with multiple observation streams. For the 2 stream case, we present a ...
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coev...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...
Markov Decision Processes (MDPs) have been extensively studied and used in the context of planning and decision-making, and many methods exist to find the optimal policy for probl...
Abstract. The heavy-tailed phenomenon that characterises the runtime distributions of backtrack search procedures has received considerable attention over the past few years. Some ...