59 search results - page 2 / 12 » Minimax Bounds for Active Learning |
COLT
2010
Springer
13 years 3 months ago
2010
Springer
Bandit convex optimization is a special case of online convex optimization with partial information. In this setting, a player attempts to minimize a sequence of adversarially gen...
COLT
2010
Springer
13 years 3 months ago
2010
Springer
We consider the problem of planning in a stochastic and discounted environment with a limited numerical budget. More precisely, we investigate strategies exploring the set of poss...
PKDD
2010
Springer
13 years 3 months ago
2010
Springer
Active learning [1] is a branch of Machine Learning in which the learning algorithm, instead of being directly provided with pairs of problem instances and their solutions (their l...
ICML
2007
IEEE
14 years 6 months ago
2007
IEEE
We study the label complexity of pool-based active learning in the agnostic PAC model. Specifically, we derive general bounds on the number of label requests made by the A2 algori...
ALT
2010
Springer
13 years 7 months ago
2010
Springer
We explore a general Bayesian active learning setting, in which the learner can ask arbitrary yes/no questions. We derive upper and lower bounds on the expected number of queries r...