Machine Learning - Special issue on inductive transfer
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A perspective view and survey of meta-learning
Artificial Intelligence Review
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Adaptive mixtures of local experts
Neural Computation
Algorithm selection as a bandit problem with unbounded losses
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Selecting Simulation Algorithm Portfolios by Genetic Algorithms
PADS '10 Proceedings of the 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
Impact of censored sampling on the performance of restart strategies
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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One aim of Meta-learning techniques is to minimize the time needed for problem solving, and the effort of parameter hand-tuning, by automating algorithm selection. The predictive model of algorithm performance needed for task often requires long training times. We address the problem in an online fashion, running multiple algorithms in parallel on a sequence of tasks, continually updating their relative priorities according to a neural model that maps their current state to the expected time to the solution. The model itself is updated at the end of each task, based on the actual performance of each algorithm. Censored sampling allows us to train the model effectively, without need of additional exploration after each task's solution. We present a preliminary experiment in which this new inter-problem technique learns to outperform a previously proposed intraproblem heuristic.