For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
Alexander L. Strehl, Lihong Li, Eric Wiewiora, Joh...
We introduce a new dimension to the widely studied on-line approximate string matching problem, by introducing an error threshold parameter so that the algorithm is allowed to mis...
Software systems are designed and engineered to process data. However, software is data too. The size and variety of today's software artifacts and the multitude of stakehold...
Compiler optimizations are often driven by specific assumptions about the underlying architecture and implementation of the target machine. For example, when targeting shared-mem...
Jack L. Lo, Susan J. Eggers, Henry M. Levy, Sujay ...
— One of the central issues in Learning to Rank (L2R) for Information Retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures ...