109 search results - page 3 / 22 » Algorithm Selection as a Bandit Problem with Unbounded Losse... |
ICDM
2007
IEEE
13 years 10 months ago
2007
IEEE
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We consider the general, widely applicable problem of selecting from n real-valued random variables a subset of size m of those with the highest means, based on as few samples as ...
GECCO
2010
Springer
13 years 9 months ago
2010
Springer
The choice of which of the available strategies should be used within the Differential Evolution algorithm for a given problem is not trivial, besides being problem-dependent and...
NIPS
2008
13 years 6 months ago
2008
We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new sel...
COLT
2005
Springer
13 years 6 months ago
2005
Springer
External regret compares the performance of an online algorithm, selecting among N actions, to the performance of the best of those actions in hindsight. Internal regret compares ...