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...
In this paper, we propose an Active Learning (AL) framework for the Multi-Task Adaptive Filtering (MTAF) problem. Specifically, we explore AL approaches to rapidly improve an MTAF...
— We consider the approximate string membership checking (ASMC) problem of extracting all the strings or substrings in a document that approximately match some string in a given ...
Currently, there are relatively few instances of "hash-and-sign" signatures in the standard model. Moreover, most current instances rely on strong and less studied assum...
— Calibrating an evolutionary algorithm (EA) means finding the right values of algorithm parameters for a given problem. This issue is highly relevant, because it has a high imp...