Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
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Minimizing development and maintenance costs in supporting persistently optimized BLAS
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Library generation for linear transforms
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Computer Generation of General Size Linear Transform Libraries
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The max K-armed bandit: a new model of exploration applied to search heuristic selection
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Multi-armed bandits with episode context
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The problem of choosing fast implementations for a class of recursive algorithms such as the fast Fourier transforms can be formulated as an optimization problem over the language generated by a suitably defined grammar. We propose a novel algorithm that solves this problem by reducing it to maximizing an objective function over the sinks of a directed acyclic graph. This algorithm valuates nodes using Monte-Carlo and grows a subgraph in the most promising directions by considering local maximum k-armed bandits. When used inside an adaptive linear transform library, it cuts down the search time by an order of magnitude compared to the existing algorithm. In some cases, the performance of the implementations found is also increased by up to 10% which is of considerable practical importance since it consequently improves the performance of all applications using the library.