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» LEO - DB2's LEarning Optimizer
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ICML
2008
IEEE
14 years 8 months ago
Learning all optimal policies with multiple criteria
We describe an algorithm for learning in the presence of multiple criteria. Our technique generalizes previous approaches in that it can learn optimal policies for all linear pref...
Leon Barrett, Srini Narayanan
ICML
2010
IEEE
13 years 5 months ago
Learning optimally diverse rankings over large document collections
Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The fe...
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Go...
AIPS
1994
13 years 9 months ago
A Planner with Quality Goal and Its Speed-up Learning for Optimization Problem
Aimsof traditional planners had beenlimited to finding a sequenceof operators rather than finding an optimal or neax-optimalfinal state. Consequent]y, the performanceimprovementsy...
Masahiko Iwamoto
ICML
2001
IEEE
14 years 8 months ago
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
Nicholas Roy, Andrew McCallum
COLT
2010
Springer
13 years 5 months ago
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradie...
John Duchi, Elad Hazan, Yoram Singer