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ML
2002
ACM
114views Machine Learning» more  ML 2002»
15 years 3 months ago
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers...
Amy McGovern, J. Eliot B. Moss, Andrew G. Barto
WSDM
2012
ACM
259views Data Mining» more  WSDM 2012»
13 years 11 months ago
Learning recommender systems with adaptive regularization
Many factorization models like matrix or tensor factorization have been proposed for the important application of recommender systems. The success of such factorization models dep...
Steffen Rendle
ICML
2008
IEEE
16 years 4 months ago
Listwise approach to learning to rank: theory and algorithm
This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function by taking individual lists as instances and minimi...
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, Ha...
ICML
2002
IEEE
16 years 4 months ago
Learning the Kernel Matrix with Semi-Definite Programming
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is perfor...
Gert R. G. Lanckriet, Nello Cristianini, Peter L. ...
JMLR
2002
89views more  JMLR 2002»
15 years 3 months ago
A Robust Minimax Approach to Classification
When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and...
Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib...