While existing learning techniques can be viewed as inducing programs from examples, most research has focused on rather narrow classes of programs, e.g., decision trees or logic ...
In this paper we develop a theoretical analysis of the performance of sampling-based fitted value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decisi...
Constraint programming is rapidly becoming the technology of choice for modelling and solving complex combinatorial problems. However, users of this technology need significant ex...
We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate...
Alina Beygelzimer, Daniel Hsu, John Langford, Tong...
We propose a new boosting algorithm. This boosting algorithm is an adaptive version of the boost by majority algorithm and combines bounded goals of the boost by majority algorith...