TRIPODS: Data Science for Improved Decision-Making: Learning in the Context of Uncertainty, Causality, Privacy, and Network Structures

TRIPODS:改善决策的数据科学:在不确定性、因果关系、隐私和网络结构的背景下学习

基本信息

  • 批准号:
    1740822
  • 负责人:
  • 金额:
    $ 149.67万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-10-01 至 2023-09-30
  • 项目状态:
    已结题

项目摘要

The researchers propose to create a center of data science for improved decision-making that combines expertise from computer science, information science, mathematics, operations research, and statistics. Their goal is to pursue basic research that will contribute to the theoretical foundations of data science. The research topics chosen have applications that can benefit society as a whole and integrate the perspectives of the disciplines that the project brings together. The five concrete research directions proposed are: Privacy and Fairness, Learning on Social Graphs, Learning to Intervene, Uncertainty Quantification, and Deep Learning. The aim of the Center is to advance knowledge in these areas and to broaden the range of disciplines and perspectives that can provide contributions to these challenging issues. The researchers plan to incorporate the community beyond Cornell through online seminars, workshops, and student conferences. The research findings will provide an urgently needed foundation for data science in several topic areas of importance to society. As the center is placed at the intersection of multiple disciplines, the intellectual merit spans all disciplines involved and findings may translate to new algorithms and approaches in each one of them. The research focus spans five core areas. 1. Privacy and Fairness. As data science becomes pervasive across many areas of society, and as it is increasingly used to aid decision-making in sensitive domains, it becomes crucial to protect individuals by guaranteeing privacy and fairness. The investigators propose to research the theoretical foundations to providing such guarantees and to surface inherent limitations. 2. Learning on Social Graphs. Many of the fundamental questions in applying data science to the interactions between individuals and larger social systems involve the social networks that underpin the connections between individuals. The researchers will develop new techniques for understanding both the structure of these networks and the processes that take place within them.3. Learning to Intervene. Data-driven approaches to learning good interventions (including policies, recommendations, and treatments) inspire challenging questions about the foundations of sequential experimental design, counterfactual reasoning, and causal inference.4. Uncertainty Quantification. Quantifying uncertainty about specific predictions or conclusions represents a key need in data science, especially when applied to decision-making with potential consequences to human subjects. The researchers will develop statistical tools and theoretical guarantees to assess the uncertainties of predictions made by popular algorithms in data science. 5. Deep Learning. Deep Learning algorithms have made impressive advances in practical settings. Although their basic building blocks are well understood, there is still ambiguity about what they learn and why they generalize so well. There are indications that they may learn data manifolds and that the type of optimization algorithm influences generalization. Advances in our theoretical understanding of these phenomena requires combined efforts from optimization, statistics, and mathematics but could lead to insights for all aspects of data science.Funds for the project come from CISE Computing and Communications Foundations, MPS Division of Mathematical Sciences, MPS Office of Multidisciplinary Activities, and Growing Convergent Research. (Convergence can be characterized as the deep integration of knowledge, techniques, and expertise from multiple fields to form new and expanded frameworks for addressing scientific and societal challenges and opportunities. This project promotes Convergence by bringing together communities representing many disciplines including mathematics, statistics, and theoretical computer science as well as engaging communities that apply data science to practical research problems.)
研究人员建议建立一个数据科学中心,结合计算机科学、信息科学、数学、运筹学和统计学的专业知识,以改进决策。他们的目标是从事有助于数据科学理论基础的基础研究。所选择的研究课题具有可以造福整个社会的应用,并将项目所带来的学科观点整合在一起。提出的五个具体研究方向是:隐私与公平、社交图学习、干预学习、不确定性量化和深度学习。该中心的目标是推进这些领域的知识,扩大学科和观点的范围,为这些具有挑战性的问题提供贡献。研究人员计划通过在线研讨会、讲习班和学生会议来整合康奈尔大学以外的社区。研究结果将为数据科学在几个对社会重要的主题领域提供急需的基础。由于该中心位于多个学科的交叉点,其知识价值跨越所有学科,研究结果可能转化为每个学科的新算法和方法。研究重点跨越五个核心领域。1. 隐私和公平。随着数据科学在社会的许多领域变得普遍,并且越来越多地用于帮助敏感领域的决策,通过保证隐私和公平来保护个人变得至关重要。研究者建议研究提供这种保障的理论基础,并指出其固有的局限性。2. 学习社交图谱。在将数据科学应用于个人和更大的社会系统之间的互动时,许多基本问题都涉及支撑个人之间联系的社会网络。研究人员将开发新的技术来了解这些网络的结构和其中发生的过程。学会干预。学习好的干预措施(包括政策、建议和治疗)的数据驱动方法激发了关于顺序实验设计、反事实推理和因果推理基础的挑战性问题。不确定性量化。量化具体预测或结论的不确定性是数据科学的一个关键需求,特别是在应用于对人类受试者有潜在影响的决策时。研究人员将开发统计工具和理论保证,以评估数据科学中流行算法所做预测的不确定性。5. 深度学习。深度学习算法在实际环境中取得了令人印象深刻的进步。尽管它们的基本组成部分已经被很好地理解了,但它们学到了什么以及为什么它们的概括性如此之好,仍然存在模糊性。有迹象表明,它们可能会学习数据流形,而优化算法的类型会影响泛化。我们对这些现象的理论理解的进步需要优化,统计学和数学的共同努力,但可能导致对数据科学各个方面的见解。该项目的资金来自CISE计算与通信基金会、MPS数学科学部、MPS多学科活动办公室和日益融合的研究。(融合的特征是来自多个领域的知识、技术和专业知识的深度整合,形成新的和扩展的框架,以应对科学和社会的挑战和机遇。该项目通过汇集代表许多学科的社区,包括数学、统计学和理论计算机科学,以及将数据科学应用于实际研究问题的社区,促进了融合。

项目成果

期刊论文数量(17)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Simplicial closure and higher-order link prediction
Leverage Score Sampling for Faster Accelerated Regression and ERM
  • DOI:
  • 发表时间:
    2017-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Naman Agarwal;S. Kakade;Rahul Kidambi;Y. Lee;Praneeth Netrapalli;Aaron Sidford
  • 通讯作者:
    Naman Agarwal;S. Kakade;Rahul Kidambi;Y. Lee;Praneeth Netrapalli;Aaron Sidford
Non-asymptotic Performance Guarantees for Neural Estimation of f-Divergences
  • DOI:
  • 发表时间:
    2021-03
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sreejith Sreekumar;Zhengxin Zhang;Ziv Goldfeld
  • 通讯作者:
    Sreejith Sreekumar;Zhengxin Zhang;Ziv Goldfeld
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
CAB:用于政策评估和学习的连续自适应混合估计器
Optimal balancing of time-dependent confounders for marginal structural models
边际结构模型的时间相关混杂因素的最佳平衡
  • DOI:
    10.1515/jci-2020-0033
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    1.4
  • 作者:
    Kallus, Nathan;Santacatterina, Michele
  • 通讯作者:
    Santacatterina, Michele
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Kilian Weinberger其他文献

Kilian Weinberger的其他文献

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{{ truncateString('Kilian Weinberger', 18)}}的其他基金

RI: AF: Small: Collaborative Research: Differentially Private Learning: From Theory to Applications
RI:AF:小型:协作研究:差异化私人学习:从理论到应用
  • 批准号:
    1618134
  • 财政年份:
    2016
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Standard Grant
III: Small: Collaborative Research: Towards Interpretable Machine Learning
III:小型:协作研究:迈向可解释的机器学习
  • 批准号:
    1525919
  • 财政年份:
    2015
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Standard Grant
CAREER: New Directions for Metric Learning
职业:度量学习的新方向
  • 批准号:
    1550179
  • 财政年份:
    2015
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant
32nd International Conference on Machine Learning (ICML 2015)
第32届国际机器学习会议(ICML 2015)
  • 批准号:
    1523346
  • 财政年份:
    2015
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Standard Grant
CAREER: New Directions for Metric Learning
职业:度量学习的新方向
  • 批准号:
    1149882
  • 财政年份:
    2012
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant

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相似海外基金

TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
  • 批准号:
    2023109
  • 财政年份:
    2020
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant
TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
  • 批准号:
    2023239
  • 财政年份:
    2020
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant
TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
  • 批准号:
    2023495
  • 财政年份:
    2020
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant
TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
  • 批准号:
    2023166
  • 财政年份:
    2020
  • 资助金额:
    $ 149.67万
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    Continuing Grant
HDR TRIPODS: Collaborative Research: Foundations of Greater Data Science
HDR TRIPODS:协作研究:大数据科学的基础
  • 批准号:
    1934962
  • 财政年份:
    2019
  • 资助金额:
    $ 149.67万
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    Continuing Grant
HDR TRIPODS: UIC Foundations of Data Science Institute
HDR TRIPODS:UIC 数据科学研究所基础
  • 批准号:
    1934915
  • 财政年份:
    2019
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: Data Science Principles of the Human-Machine Convergence
HDR TRIPODS:人机融合的数据科学原理
  • 批准号:
    1934924
  • 财政年份:
    2019
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: UT Austin Institute on the Foundations of Data Science
HDR TRIPODS:UT Austin 数据科学基础研究所
  • 批准号:
    1934932
  • 财政年份:
    2019
  • 资助金额:
    $ 149.67万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representations, and Algorithms
HDR TRIPODS:数据科学的创新:集成随机建模、数据表示和算法
  • 批准号:
    1934964
  • 财政年份:
    2019
  • 资助金额:
    $ 149.67万
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HDR TRIPODS: UC Davis TETRAPODS Institute of Data Science
HDR TRIPODS:加州大学戴维斯分校 TETRAPODS 数据科学研究所
  • 批准号:
    1934568
  • 财政年份:
    2019
  • 资助金额:
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