Communications of the ACM
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
SIAM Journal on Computing
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Information and Computation
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Efficient Reinforcement Learning in Factored MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Theoretical Computer Science - Special issue: Algorithmic learning theory
Experience-efficient learning in associative bandit problems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Maximizing classifier utility when training data is costly
ACM SIGKDD Explorations Newsletter
Worst-Case Analysis of Selective Sampling for Linear Classification
The Journal of Machine Learning Research
Efficient structure learning in factored-state MDPs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Minimizing regret with label efficient prediction
IEEE Transactions on Information Theory
An object-oriented representation for efficient reinforcement learning
Proceedings of the 25th international conference on Machine learning
Robust bounds for classification via selective sampling
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Provably Efficient Learning with Typed Parametric Models
The Journal of Machine Learning Research
Exploring compact reinforcement-learning representations with linear regression
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Reducing reinforcement learning to KWIK online regression
Annals of Mathematics and Artificial Intelligence
Incremental learning of relational action models in noisy environments
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Selective sampling and active learning from single and multiple teachers
The Journal of Machine Learning Research
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We introduce a learning framework that combines elements of the well-known PAC and mistake-bound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is true in reinforcement-learning and active-learning problems. We catalog several KWIK-learnable classes and open problems.