Collaborative Research: PPoSS: Planning: Scalable Systems for Probabilistic Programming

协作研究:PPoSS:规划:概率编程的可扩展系统

基本信息

  • 批准号:
    2029016
  • 负责人:
  • 金额:
    $ 12.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-10-01 至 2022-09-30
  • 项目状态:
    已结题

项目摘要

Statistical methods have had great successes for exploring data, making predictions, and solving problems in a wide range of problems. But in the world of big data, methods need to be scalable, so as to handle larger problems while modeling the real-world problems of messy and nonrepresentative data. The project’s novelties are developments in software and hardware facilitating full-stack integration of Bayesian inference to allow complex and realistic models to be fit to large datasets. The project's impacts are in many areas of pure and applied science, including fields as diverse as epidemiology, genetics, and political science, which are challenging because they are dense in parameters rather than in data. Examples include models for disease progression and drug development, decision making under uncertainty, and trends in public opinion.The project is exploring probabilistic programming, including hardware, high-performance computing, programming languages and compilers, and algorithms. The ultimate goal is to develop the tools necessary for an efficient, and scalable Bayesian workflow, building on the existing success of the open-source probabilistic programming language Stan. The team of researchers on this project are working on explorations of algorithms (model validation for approximate inference), programming languages and compilers (automating of approximate algorithms and advanced performance profiling), systems (probabilistic programming for streaming data), high-performance computing (parallel processing and GPUs), and hardware (exploring domain-specific hardware for Bayesian computation).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
统计方法在探索数据、进行预测和解决各种问题方面取得了巨大的成功。但在大数据的世界中,方法需要可扩展,以便在对杂乱和非代表性数据的现实问题建模时处理更大的问题。 该项目的新颖之处是软件和硬件的开发,促进贝叶斯推理的全栈集成,以允许复杂而现实的模型适合大型数据集。 该项目的影响涉及许多纯科学和应用科学领域,包括流行病学、遗传学和政治学等不同领域,这些领域具有挑战性,因为它们的参数密集而不是数据密集。 例如疾病进展和药物开发的模型,不确定性下的决策以及公众舆论的趋势。该项目正在探索概率编程,包括硬件,高性能计算,编程语言和编译器以及算法。 最终目标是开发一个有效的,可扩展的贝叶斯工作流程所需的工具,建立在开源概率编程语言Stan的现有成功基础上。 该项目的研究人员团队正在探索算法(近似推理的模型验证),编程语言和编译器(近似算法和高级性能分析的自动化),系统(流数据的概率编程),高性能计算(并行处理和GPU),和硬件(探索用于贝叶斯计算的特定领域硬件)该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Statically bounded-memory delayed sampling for probabilistic streams
概率流的静态有界内存延迟采样
Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling
Semi-symbolic inference for efficient streaming probabilistic programming
  • DOI:
    10.1145/3563347
  • 发表时间:
    2022-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Eric Hamilton Atkinson;Charles Yuan;Guillaume Baudart;Louis Mandel;Michael Carbin
  • 通讯作者:
    Eric Hamilton Atkinson;Charles Yuan;Guillaume Baudart;Louis Mandel;Michael Carbin
Simplifying dependent reductions in the polyhedral model
简化多面体模型中的相关约简
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Tamara Broderick其他文献

Redshift Accuracy Requirements for Future Supernova and Number Count Surveys
未来超新星和计数巡天的红移精度要求
  • DOI:
    10.1086/424726
  • 发表时间:
    2004
  • 期刊:
  • 影响因子:
    0
  • 作者:
    D. Huterer;A. Kim;L. Krauss;Tamara Broderick
  • 通讯作者:
    Tamara Broderick
Comment: Nonparametric Bayes Modeling of Populations of Networks
Variational Bayes for Merging Noisy Databases
用于合并噪声数据库的变分贝叶斯
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tamara Broderick;R. Steorts
  • 通讯作者:
    R. Steorts
Covariance Matrices and Influence Scores for Mean Field Variational Bayes
平均场变分贝叶斯的协方差矩阵和影响分数
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ryan Giordano;Tamara Broderick
  • 通讯作者:
    Tamara Broderick
Covariance Matrices for Mean Field Variational Bayes
平均场变分贝叶斯的协方差矩阵
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ryan Giordano;Tamara Broderick
  • 通讯作者:
    Tamara Broderick

Tamara Broderick的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Tamara Broderick', 18)}}的其他基金

CAREER: Robust, scalable, reliable machine learning
职业:稳健、可扩展、可靠的机器学习
  • 批准号:
    1750286
  • 财政年份:
    2018
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Workshop for Women in Machine Learning
机器学习女性研讨会
  • 批准号:
    1833154
  • 财政年份:
    2018
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant

相似国自然基金

Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 批准年份:
    2024
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
Cell Research
  • 批准号:
    31224802
  • 批准年份:
    2012
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research
  • 批准号:
    31024804
  • 批准年份:
    2010
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research (细胞研究)
  • 批准号:
    30824808
  • 批准年份:
    2008
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
  • 批准号:
    2316161
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: LARGE: Research into the Use and iNtegration of Data Movement Accelerators (RUN-DMX)
协作研究:PPoSS:大型:数据移动加速器 (RUN-DMX) 的使用和集成研究
  • 批准号:
    2316176
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
  • 批准号:
    2316158
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
  • 批准号:
    2316201
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
  • 批准号:
    2316203
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: LARGE: Research into the Use and iNtegration of Data Movement Accelerators (RUN-DMX)
协作研究:PPoSS:大型:数据移动加速器 (RUN-DMX) 的使用和集成研究
  • 批准号:
    2316177
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
  • 批准号:
    2316202
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
Collaborative Research: PPoSS: LARGE: General-Purpose Scalable Technologies for Fundamental Graph Problems
合作研究:PPoSS:大型:解决基本图问题的通用可扩展技术
  • 批准号:
    2316235
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: LARGE: Principles and Infrastructure of Extreme Scale Edge Learning for Computational Screening and Surveillance for Health Care
合作研究:PPoSS:大型:用于医疗保健计算筛查和监视的超大规模边缘学习的原理和基础设施
  • 批准号:
    2406572
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
  • 批准号:
    2316159
  • 财政年份:
    2023
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了