In this paper we introduce a paradigm for learning in the limit of potentially infinite languages from all positive data and negative counterexamples provided in response to the ...
d Abstract] Dana Angluin James Aspnes Jiang Chen Yinghua Wu We propose a new model for exact learning of acyclic circuits using experiments in which chosen values may be assigned ...
Dana Angluin, James Aspnes, Jiang Chen, Yinghua Wu
This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most t...
We formulate and study a decentralized multi-armed bandit (MAB) problem. There are distributed players competing for independent arms. Each arm, when played, offers i.i.d. reward a...
This paper presents a novel paradigm for learning languages that consists of mapping strings to an appropriate high-dimensional feature space and learning a separating hyperplane i...