Proceedings of the 16th ACM SIGPLAN International Haskell Symposium最新文献

The Essence of Reactivity 反应性的本质

Proceedings of the 16th ACM SIGPLAN International Haskell Symposium Pub Date : 2023-08-30 DOI: 10.1145/3609026.3609727

Ivan Perez, F. Dedden

引用次数: 0

falsify: Internal Shrinking Reimagined for Haskell 伪造:内部萎缩为哈斯克尔重新想象

Proceedings of the 16th ACM SIGPLAN International Haskell Symposium Pub Date : 2023-08-30 DOI: 10.1145/3609026.3609733

Edsko de Vries

引用次数: 0

This Is Driving Me Loopy: Efficient Loops in Arrowized Functional Reactive Programs 这让我发疯:箭头化函数式反应程序中的高效循环

Proceedings of the 16th ACM SIGPLAN International Haskell Symposium Pub Date : 2023-08-30 DOI: 10.1145/3609026.3609726

Finnbar Keating, Michael B. Gale

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Haskell for Choice-Based Learning (Keynote) 基于选择学习的Haskell(主题演讲)

Proceedings of the 16th ACM SIGPLAN International Haskell Symposium Pub Date : 2023-08-30 DOI: 10.1145/3609026.3615580

Ningning Xie

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引用次数: 0

Haskell Library for Safer Virtual Machine Introspection (Experience Report) Haskell库用于更安全的虚拟机自省(体验报告)

Proceedings of the 16th ACM SIGPLAN International Haskell Symposium Pub Date : 2023-08-30 DOI: 10.1145/3609026.3609732

Takato Otsuka, H. Iwasaki

引用次数: 0

An Exceptional Actor System (Functional Pearl) 一个特殊角色系统(功能珍珠)

Proceedings of the 16th ACM SIGPLAN International Haskell Symposium Pub Date : 2023-07-20 DOI: 10.1145/3609026.3609728

Patrick Redmond, L. Kuper

引用次数: 0

Effect Handlers for Programmable Inference 可编程推理的效果处理程序

Proceedings of the 16th ACM SIGPLAN International Haskell Symposium Pub Date : 2023-03-02 DOI: 10.1145/3609026.3609729

Minh Nguyen, R. Perera, M. Wang, S. Ramsay

{"title":"Effect Handlers for Programmable Inference","authors":"Minh Nguyen, R. Perera, M. Wang, S. Ramsay","doi":"10.1145/3609026.3609729","DOIUrl":"https://doi.org/10.1145/3609026.3609729","url":null,"abstract":"Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particular probabilistic model or problem, sometimes called inference programming. Most inference frameworks are implemented in languages that lack a disciplined approach to side effects, which can result in monolithic implementations where the structure of the algorithms is obscured and inference programming is hard. Functional programming with typed effects offers a more structured and modular foundation for programmable inference, with monad transformers being the primary structuring mechanism explored to date. This paper presents an alternative approach to inference programming based on algebraic effects. Using effect signatures to specify the key operations of the algorithms, and effect handlers to modularly interpret those operations for specific variants, we develop two abstract algorithms, or inference patterns, representing two important classes of inference: Metropolis-Hastings and particle filtering. We show how our approach reveals the algorithms’ high-level structure, and makes it easy to tailor and recombine their parts into new variants. We implement the two inference patterns as a Haskell library, and discuss the pros and cons of algebraic effects vis-à-vis monad transformers as a structuring mechanism for modular imperative algorithm design.","PeriodicalId":184785,"journal":{"name":"Proceedings of the 16th ACM SIGPLAN International Haskell Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132473630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1