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43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of programming Languages (POPL)

作者： McKinley, Kathryn S. Microsoft Res Redmond WA USA

ISBN: (纸本)9781450335492

Computing has entered the era of uncertain data, in which hardware and software generate and reason about estimates. Applications use estimates from sensors, machine learning, big data, humans, and approximate hardware and software. Unfortunately, developers face pervasive correctness, programmability, and optimization problems due to estimates. Most programming languages unfortunately make these problems worse. We propose a new programming abstraction called Uncertain < T > embedded into languages, such as C#, C++, Java, Python, and JavaScript. Applications that consume estimates use familiar discrete operations for their estimates;overloaded conditional operators specify hypothesis tests and applications use them control false positives and negatives;and new compositional operators express domain knowledge. By carefully restricting the expressiveness, the runtime automatically implements correct statistical reasoning at conditionals, relieving developers of the need to implement or deeply understand statistics. We demonstrate substantial programmability, correctness, and efficiency benefits of this programming model for GPS sensor navigation, approximate computing, machine learning, and xBox.

关键词： Languages Verification Measurement programming with estimates Probabilistic programming Bayesian logic Lazy evaluation

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programming the World of Uncertain Things (Keynote)

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ACM SIGPLAN NOTICES 2016年第1期51卷 1-2页

作者： McKinley, Kathryn S. Microsoft Res Redmond WA USA

关键词： Languages Verification Measurement programming with estimates Probabilistic programming Bayesian logic Lazy evaluation

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programming Uncertain hings 16

Programming Uncertain <T>hings

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Proceedings of the Twenty-First International Conference on Architectural Support for programming Languages and Operating Systems

作者： Kathryn S. McKinley Microsoft Research Redmond WA USA

ISBN: (纸本)9781450340915

Innovation flourishes with good abstractions. For instance, codification of the IEEE Floating Point standard in 1985 was critical to the subsequent success of scientific computing. programming languages currently lack appropriate abstractions for uncertain data. Applications already use estimates from sensors, machine learning, big data, humans, and approximate algorithms, but most programming languages do not help developers address correctness, programmability, and optimization problems due to *** address these problems, we propose a new programming abstraction called Uncertain embedded into languages, such as C#, C++, Java, Python, and JavaScript. Applications use familiar discrete operations for estimates with Uncertain. Overloaded conditional operators specify hypothesis tests and applications use them to control false positives and negatives. A simple compositional operator expresses domain knowledge. We carefully restrict expressiveness such that we can build a runtime that implements correct statistical reasoning at conditionals. Our system relieves developers of the need to implement or deeply understand statistics. We demonstrate substantial programmability, correctness, and efficiency benefits of this programming model for GPS sensor navigation, approximate computing, machine learning, and *** encourage the community to develop and use abstractions for estimates.

关键词： probabilistic programming bayesian logic lazy evaluation languages programming with estimates measurement verification

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