We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a s...
Matthew J. Streeter, Daniel Golovin, Stephen F. Sm...
Current studies have demonstrated that the representational power of predictive state representations (PSRs) is at least equal to the one of partially observable Markov decision p...
Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chai...
At Crypto 2007, Joux and Peyrin showed that the boomerang attack, a classical tool in block cipher cryptanalysis, can also be very useful when analyzing hash functions. They applie...
Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
The problem of identifying approximately duplicate objects in databases is an essential step for the information integration process. Most existing approaches have relied on gener...