Nonparametric Methodology for Learning from People: Inference, Algorithms, and Optimality

向人学习的非参数方法：推理、算法和最优性

• 批准号: 2210734
• 负责人: Ashwin Pananjady
• 金额: $ 20万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210734&HistoricalAwards=false
• 关键词: Nonparametric; Methodology; Learning; People; Inference

Learning from people represents a new and exciting paradigm for research in statistics and data science and is useful both in understanding behavioral patterns (for example in marketing) and for informing interventions (for example in education). Data from humans can also be elicited to inform various downstream tasks, with crowdsourcing being routinely used to collect data across applications spanning bioinformatics, epidemiology, computer vision, and environmental modeling. A common characteristic of such data is its scale and noisiness; having flexible and interpretable models for such data is broadly useful in downstream decision-making. The investigators aim to develop and thoroughly study flexible classes of models and methods for drawing inferences from large-scale data collected from people in a variety of such contexts.Specifically, the investigations will focus on developing three key facets of such nonparametric methodology: (a) Computationally efficient, assumption-lean methods to fit expressive models to data; (b) Equipping models to integrate application-specific information in a general-purpose fashion; and (c) Developing models and methods that accommodate dynamically varying data streams. The research formulates a host of theoretical and methodological questions whose solutions would constitute fundamental progress in nonparametric inference, while touching upon practical and timely issues such as fairness in ranking systems. This project will also provide training and research opportunities for the next generation of data scientists by encouraging them to model the entire data analysis pipeline, from data collection to inference.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.

向人学习代表了统计学和数据科学研究的一个令人兴奋的新范式，在理解行为模式（例如在市场营销中）和告知干预措施（例如在教育中）方面都很有用。来自人类的数据也可以为各种下游任务提供信息，众包通常用于收集跨生物信息学、流行病学、计算机视觉和环境建模等应用程序的数据。这类数据的一个共同特点是规模大、噪声大；为这些数据提供灵活且可解释的模型在下游决策中非常有用。研究人员的目标是开发和深入研究灵活的模型和方法，以便从各种此类背景下从人们收集的大规模数据中得出推论。具体地说，调查将集中于发展这种非参数方法的三个关键方面：(a)计算效率高、假设少的方法，使表达模型适合于数据；(b)使模型以通用方式综合具体应用的资料；(c)发展适应动态变化的数据流的模型和方法。该研究提出了一系列理论和方法上的问题，这些问题的解决方案将构成非参数推理的基本进展，同时触及了诸如排名系统公平性等实际和及时的问题。该项目还将为下一代数据科学家提供培训和研究机会，鼓励他们对从数据收集到推理的整个数据分析管道进行建模。该奖项反映了美国国家科学基金会的法定使命，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

Sharp analysis of EM for learning mixtures of pairwise differences

敏锐的 EM 分析，用于学习成对差异的混合

• 发表时间: 2023
• 期刊: Proceedings of Thirty Sixth Conference on Learning Theory
• 作者: Dhawan, Abhishek;Mao, Cheng;Pananjady, Ashwin
• 通讯作者: Pananjady, Ashwin

Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning

低秩度量学习的感知调整查询和倒置测量范式

• 发表时间: 2023
• 期刊: Advances in neural information processing systems
• 作者: Austin Xu;Andrew McRae;Jingyan Wang;Mark Davenport;Ashwin Pananjady
• 通讯作者: Ashwin Pananjady

Modeling and Correcting Bias in Sequential Evaluation

序贯评估中的建模和纠正偏差

• DOI: 10.1145/3580507.3597747
• 发表时间: 2023
• 期刊: 24th ACM Conference on Economics and Computation
• 作者: Wang, Jingyan;Pananjady, Ashwin
• 通讯作者: Pananjady, Ashwin

Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits

• 发表时间: 2021-06
• 作者: Wenshuo Guo;Kumar Krishna Agrawal;Aditya Grover;Vidya Muthukumar;A. Pananjady

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation

马尔可夫线性随机逼近的最优且依赖于实例的保证

• 发表时间: 2022
• 期刊: Proceedings of the 35th Conference on Learning Theory (COLT2022
• 作者: Mou, Wenlong;Pananjady, Ashwin;Wainwright, Martin J.;Bartlett, Peter L.
• 通讯作者: Bartlett, Peter L.