Conference: Inaugural CAMDA Conference

会议:首届 CAMDA 会议

基本信息

  • 批准号:
    2329268
  • 负责人:
  • 金额:
    $ 3.52万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-05-15 至 2024-04-30
  • 项目状态:
    已结题

项目摘要

Machine learning has recently attracted renewed attention following the release of highly versatile large language models such as Chat-GPT and Bard. As concerns about the social and economic impact of technical advances mount, many of machine learning's most successful tools remain poorly understood and their inner working obscure even to their creators. We believe that a healthy and durable society must be built on well-understood, responsible, and interpretable principles. This conference is dedicated to the mathematical foundations of machine learning and its application in analytically tractable settings with the aim of building the tools to better understand the powerful, but inscrutable emerging tools and to discuss interpretable alternative approaches.Texas A&M University is a historical stronghold of approximation theory, which itself underlies learning theory. Indeed, the question 'how well can a function be approximated in general?' arguably precedes the question 'how well can a function be approximated from point values?'. It is an objective of this conference (https://sites.google.com/tamu.edu/camda-conference/) to place rigorous mathematical analysis at the center of future developments in data science in order to guide socially and environmentally responsible progress. Four plenary speakers from Departments of Mathematics, Electrical and Computer Engineering and Computer Science will address an interdisciplinary audience in this effort. An 'open problems' session is planned for the discussion and dissemination of important open problems in this effort, with the specific goal of attracting junior researchers.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.

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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Simon Foucart其他文献

for Two Intersected Centered
对于两个相交的中心
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Simon Foucart;†. ChunyangLiao
  • 通讯作者:
    †. ChunyangLiao
Radius of information for two intersected centered hyperellipsoids and implications in optimal recovery from inaccurate data
两个相交的中心超椭球体的信息半径以及对不准确数据的最佳恢复的影响
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Simon Foucart;Chunyang Liao
  • 通讯作者:
    Chunyang Liao
Optimization-Aided Construction of Multivariate Chebyshev Polynomials
多元切比雪夫多项式的优化辅助构造
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mareike Dressler;Simon Foucart;E. Klerk;Mioara Joldes;Jean;Yuan Xu
  • 通讯作者:
    Yuan Xu
Worst-Case Learning under a Multi-fidelity Model
多保真度模型下的最坏情况学习
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Simon Foucart;Nicolas Hengartner
  • 通讯作者:
    Nicolas Hengartner
Linearly Embedding Sparse Vectors from $ell_2$ to $ell_1$ via Deterministic Dimension-Reducing Maps
通过确定性降维映射将稀疏向量从 $ell_2$ 线性嵌入到 $ell_1$
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Simon Foucart
  • 通讯作者:
    Simon Foucart

Simon Foucart的其他文献

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{{ truncateString('Simon Foucart', 18)}}的其他基金

CDS&E-MSS: Optimal Recovery in the Age of Data Science
CDS
  • 批准号:
    2053172
  • 财政年份:
    2021
  • 资助金额:
    $ 3.52万
  • 项目类别:
    Standard Grant
CDS&E-MSS: Recovery of High-Dimensional Structured Functions
CDS
  • 批准号:
    1622134
  • 财政年份:
    2016
  • 资助金额:
    $ 3.52万
  • 项目类别:
    Standard Grant
ATD: Improving Analysis of Microbial Mixtures through Sparse Reconstruction Algorithms and Statistical Inference
ATD:通过稀疏重建算法和统计推断改进微生物混合物的分析
  • 批准号:
    1120622
  • 财政年份:
    2011
  • 资助金额:
    $ 3.52万
  • 项目类别:
    Standard Grant

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