21 citations · 87 across the 20 of their papers we have counts for
4 papers · 1 filter
Effective sketching methods for value function approximation
Yangchen Pan, Erfan Sadeqi Azer, Martha White
High-dimensional representations, such as radial basis function networks or tile coding, are common choices for policy evaluation in reinforcement learning. Learning with such high…
Learning Sparse Representations in Reinforcement Learning with Sparse Coding
Lei Le, Raksha Kumaraswamy, Martha White
A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of spar…
Data-Efficient Policy Evaluation Through Behavior Policy Search
Josiah P. Hanna, Philip S. Thomas, Peter Stone +1
We consider the task of evaluating a policy for a Markov decision process (MDP). The standard unbiased technique for evaluating a policy is to deploy the policy and observe its per…
Recovering True Classifier Performance in Positive-Unlabeled Learning
Shantanu Jain, Martha White, Predrag Radivojac
A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier…