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Algorithm Selection for Combinatorial Search Problems: A Survey
The Algorithm Selection Problem is concerned with selecting the best alg...
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LLAMA: Leveraging Learning to Automatically Manage Algorithms
Algorithm portfolio and selection approaches have achieved remarkable im...
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Automated Algorithm Selection: Survey and Perspectives
It has long been observed that for practically any computational problem...
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The Algorithm Selection Competition Series 2015-17
The algorithm selection problem is to choose the most suitable algorithm...
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Unshuffling fields in data formats
Data format reverse engineering commonly involves identifying conserved ...
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A Repository of Conversational Datasets
Progress in Machine Learning is often driven by the availability of larg...
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A Comparative Evaluation of Quantification Methods
Quantification represents the problem of predicting class distributions ...
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ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.
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