We welcome submissions of innovative work on systems that are self adaptive, systems that improve their own performance, or systems that apply logical, statistical, probabilistic or other formalisms to the analysis of data, learning predictive models, or interaction with the environment. We welcome innovative applications, theoretical contributions, carefully evaluated empirical studies, and we particularly welcome work that combines all of these...
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We welcome submissions of innovative work on systems that are self adaptive, systems that improve their own performance, or systems that apply logical, statistical, probabilistic or other formalisms to the analysis of data, learning predictive models, or interaction with the environment. We welcome innovative applications, theoretical contributions, carefully evaluated empirical studies, and we particularly welcome work that combines all of these. We also encourage submissions that bridge the gap between machine learning and other fields of research.
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