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GECCO
2003
Springer
114views Optimization» more  GECCO 2003»
15 years 5 months ago
Integration of Genetic Programming and Reinforcement Learning for Real Robots
Shotaro Kamio, Hideyuki Mitsuhasi, Hitoshi Iba
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
13 years 2 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
COLT
2008
Springer
15 years 1 months ago
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization
We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O ( T) regret. The setting is a natural general...
Jacob Abernethy, Elad Hazan, Alexander Rakhlin
ICML
1996
IEEE
16 years 19 days ago
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning
Research in reinforcementlearning (RL)has thus far concentrated on two optimality criteria: the discounted framework, which has been very well-studied, and the averagereward frame...
Sridhar Mahadevan
PPSN
2004
Springer
15 years 5 months ago
Coupling of Evolution and Learning to Optimize a Hierarchical Object Recognition Model
Abstract. A key problem in designing artificial neural networks for visual object recognition tasks is the proper choice of the network architecture. Evolutionary optimization met...
Georg Schneider, Heiko Wersing, Bernhard Sendhoff,...