We formulate and study search algorithms that consider a user’s prior interactions with a wide variety of content to personalize that user’s current Web search. Rather than re...
In order to allow a comparison of (otherwise incomparable) sets, many evolutionary multiobjective optimizers use indicator functions to guide the search and to evaluate the perfor...
Private approximation of search problems deals with finding approximate solutions to search problems while disclosing as little information as possible. The focus of this work is ...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Abstract. Kernel based methods (such as k-nearest neighbors classifiers) for AI tasks translate the classification problem into a proximity search problem, in a space that is usu...