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Syntactic Foundations for Machine Learning

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Recent years have seen a rising interest in probabilistic programming languages for applying machine learning to data analysis problems. The promise of these languages is that users only need to write declarative specifications of their probabilistic models, leaving the details to the compiler regarding how to produce customized inference algorithms, which saves countless hours of development effort. In this talk, we present and argue for a new language that exhibits several features that are not simultaneously present in any existing language. These features include the ability to express optimization problems, a rigorous treatment of probability density functions, and a formal language definition. We conclude with some thoughts and questions about the design of future “languages for machine learning”.

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