CAREER: A New Neat Framework for Statistical Machine Learning
职业:统计机器学习的新简洁框架
基本信息
- 批准号:1661755
- 负责人:
- 金额:$ 22.94万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-09-01 至 2019-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The pendulum in Artificial Intelligence (AI) research has periodically swung from so called "neat" or mathematically rigorous approaches, and "scruffy" or more adhoc approaches. In recent years, real-world data across varied fields of science and engineering are increasingly complex, and involve a large number of variables, which has resulted in a surge of scruffier methods. This proposal develops a general "neat" framework for such modern settings by leveraging state of the art developments in two of the most popular subfields of machine learning methods: graphical models and high-dimensional statistical methods. These developments have in common that a complex model parameter is expressed as a superposition of simple components, which is then leveraged for tractable inference and learning.Our unified framework results not only in a unified picture of these developments but also provides newer methods to work with such high-dimensional data. The research thus impacts problems across science and engineering wherever statistical machine learning approaches are being used (such as genomics, natural language processing and image analysis, to name a few). The work on a unified framework for statistical machine learning problems is highly coupled with a push for imparting training to students on what we call "comptastical" thinking. This combines both computational and statistical thinking required for addressing the problems of limited computation and limited data inherent in modern statistical AI application domains. The proposal also develops an infrastructure for component-based courses with relationally organized lecture module components.
人工智能(AI)研究的钟摆周期性地从所谓的“整洁”或数学严谨的方法,到“邋遢”或更临时的方法之间摇摆。近年来,科学和工程各个领域的真实数据变得越来越复杂,并且涉及大量变量,这导致了更复杂的方法激增。本提案通过利用机器学习方法的两个最流行的子领域:图形模型和高维统计方法的最新发展,为这种现代设置开发了一个通用的“整洁”框架。这些发展的共同点是,将复杂的模型参数表示为简单组件的叠加,然后利用它进行易于处理的推理和学习。我们的统一框架不仅形成了这些开发的统一图景,而且还提供了处理这些高维数据的新方法。因此,该研究影响了使用统计机器学习方法的科学和工程领域的问题(例如基因组学,自然语言处理和图像分析等)。为统计机器学习问题建立统一框架的工作,与向学生传授我们所谓的“对比”思维训练的努力高度相关。这结合了解决现代统计人工智能应用领域固有的有限计算和有限数据问题所需的计算和统计思维。该提案还为基于组件的课程开发了一个基础设施,该基础设施具有相互组织的讲座模块组件。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Pradeep Ravikumar其他文献
Ordinal Graphical Models: A Tale of Two Approaches
序数图形模型:两种方法的故事
- DOI:
10.5555/3305890.3306018 - 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
A. Suggala;Eunho Yang;Pradeep Ravikumar - 通讯作者:
Pradeep Ravikumar
XMRF: an R package to fit Markov Networks to high-throughput genetics data
XMRF:一个 R 包,用于使马尔可夫网络适应高通量遗传学数据
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Ying;Genevera I. Allen;Yulia Baker;Eunho Yang;Pradeep Ravikumar;Zhandong Liu - 通讯作者:
Zhandong Liu
Deep Density Destructors
深度密度破坏函数
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
David I. Inouye;Pradeep Ravikumar - 通讯作者:
Pradeep Ravikumar
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
非参数稀疏分层模型描述 V1 fMRI 对自然图像的响应
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Pradeep Ravikumar;Vincent Q. Vu;Bin Yu;Thomas Naselaris;Kendrick Norris Kay;J. Gallant - 通讯作者:
J. Gallant
Learning Graphs with a Few Hubs - Supplementary
用几个中心学习图 - 补充
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Rashish Tandon;Pradeep Ravikumar - 通讯作者:
Pradeep Ravikumar
Pradeep Ravikumar的其他文献
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{{ truncateString('Pradeep Ravikumar', 18)}}的其他基金
RI: Medium: Foundations of Self-Supervised Learning Through the Lens of Probabilistic Generative Models
RI:媒介:通过概率生成模型的视角进行自我监督学习的基础
- 批准号:
2211907 - 财政年份:2022
- 资助金额:
$ 22.94万 - 项目类别:
Standard Grant
Collaborative Research: RI: Medium: A Rigorous, General Framework for Tractable Learning of Large-Scale DAGs from Data
协作研究:RI:Medium:从数据中轻松学习大规模 DAG 的严格通用框架
- 批准号:
1955532 - 财政年份:2020
- 资助金额:
$ 22.94万 - 项目类别:
Continuing Grant
RI: Small: Non-parametric Machine Learning in the Age of Deep and High-Dimensional Models
RI:小:深度和高维模型时代的非参数机器学习
- 批准号:
1909816 - 财政年份:2019
- 资助金额:
$ 22.94万 - 项目类别:
Standard Grant
Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting
合作研究:基于物理的机器学习用于次季节气候预测
- 批准号:
1934584 - 财政年份:2019
- 资助金额:
$ 22.94万 - 项目类别:
Continuing Grant
BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data
BIGDATA:F:DKA:协作研究:时空气候数据的高维统计机器学习
- 批准号:
1664720 - 财政年份:2016
- 资助金额:
$ 22.94万 - 项目类别:
Standard Grant
Collaborative Research: Statistical Methods for Integrated Analysis of High-Throughput Biomedical Data
合作研究:高通量生物医学数据综合分析的统计方法
- 批准号:
1661802 - 财政年份:2016
- 资助金额:
$ 22.94万 - 项目类别:
Continuing Grant
BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data
BIGDATA:F:DKA:协作研究:时空气候数据的高维统计机器学习
- 批准号:
1447574 - 财政年份:2014
- 资助金额:
$ 22.94万 - 项目类别:
Standard Grant
Collaborative Research: Statistical Methods for Integrated Analysis of High-Throughput Biomedical Data
合作研究:高通量生物医学数据综合分析的统计方法
- 批准号:
1264033 - 财政年份:2013
- 资助金额:
$ 22.94万 - 项目类别:
Continuing Grant
RI: Small: Collaborative Research: Statistical ranking theory without a canonical loss
RI:小:协作研究:没有典型损失的统计排名理论
- 批准号:
1320894 - 财政年份:2013
- 资助金额:
$ 22.94万 - 项目类别:
Standard Grant
CAREER: A New Neat Framework for Statistical Machine Learning
职业:统计机器学习的新简洁框架
- 批准号:
1149803 - 财政年份:2012
- 资助金额:
$ 22.94万 - 项目类别:
Continuing Grant
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