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» Approximation algorithms for budgeted learning problems
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NIPS
2001
14 years 11 months ago
Model-Free Least-Squares Policy Iteration
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Michail G. Lagoudakis, Ronald Parr
VLDB
2006
ACM
162views Database» more  VLDB 2006»
15 years 10 months ago
Dependency trees in sub-linear time and bounded memory
We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other u...
Dan Pelleg, Andrew W. Moore
IPPS
2000
IEEE
15 years 2 months ago
Reduction Optimization in Heterogeneous Cluster Environments
Network of workstation (NOW) is a cost-effective alternative to massively parallel supercomputers. As commercially available off-the-shelf processors become cheaper and faster, ...
Pangfeng Liu, Da-Wei Wang
ICML
2007
IEEE
15 years 10 months ago
Online discovery of similarity mappings
We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated ...
Alexander Rakhlin, Jacob Abernethy, Peter L. Bartl...
ALT
2008
Springer
15 years 6 months ago
Smooth Boosting for Margin-Based Ranking
We propose a new boosting algorithm for bipartite ranking problems. Our boosting algorithm, called SoftRankBoost, is a modification of RankBoost which maintains only smooth distri...
Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, Masa...