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PLDI
2003
ACM
13 years 12 months ago
A comparison of empirical and model-driven optimization
Empirical program optimizers estimate the values of key optimization parameters by generating different program versions and running them on the actual hardware to determine which...
Kamen Yotov, Xiaoming Li, Gang Ren, Michael Cibuls...
JMLR
2010
125views more  JMLR 2010»
13 years 1 months ago
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
The presence of asymmetry in the misclassification costs or class prevalences is a common occurrence in the pattern classification domain. While much interest has been devoted to ...
Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra
IPPS
2007
IEEE
14 years 1 months ago
POET: Parameterized Optimizations for Empirical Tuning
The excessive complexity of both machine architectures and applications have made it difficult for compilers to statically model and predict application behavior. This observatio...
Qing Yi, Keith Seymour, Haihang You, Richard W. Vu...
CC
2008
Springer
172views System Software» more  CC 2008»
13 years 8 months ago
Efficient Context-Sensitive Shape Analysis with Graph Based Heap Models
The performance of heap analysis techniques has a significant impact on their utility in an optimizing compiler. Most shape analysis techniques perform interprocedural dataflow ana...
Mark Marron, Manuel V. Hermenegildo, Deepak Kapur,...
JCS
2002
103views more  JCS 2002»
13 years 6 months ago
Toward Cost-Sensitive Modeling for Intrusion Detection and Response
Intrusion detection systems (IDSs) must maximize the realization of security goals while minimizing costs. In this paper, we study the problem of building cost-sensitive intrusion...
Wenke Lee, Wei Fan, Matthew Miller, Salvatore J. S...