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

基本信息

批准号：
1955532
负责人：
Pradeep Ravikumar
金额：
$ 79.99万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-15 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955532&HistoricalAwards=false
关键词：
Collaborative Research RI Medium Rigorous

项目摘要

Recent advances in machine learning and artificial intelligence owe much of their success to the development of algorithms that learn complicated relationships and understanding complex phenomena from massive datasets. These algorithms have been successfully applied on a diverse array of applications, including medicine, genetics, robotics, marketing, finance, and, increasingly, in societal applications. Despite their many successes, however, these applications continue to suffer from security, transparency, fairness, and interpretability problems. Many of these practical challenges can be traced back to well-known limitations with respect to interpretability, causality, and false discoveries. At the same time, substantial progress has been made in recent years in our understanding of these practical challenges in relatively simple settings with only a few factors and comparatively simple models. This research seeks to integrate these efforts, in order to provide a flexible framework for flexible, interpretable, causal modeling from high-dimensional, complex datasets. The investigated approach specifically seeks to avoid spurious correlations that commonly appear in complex datasets, while retaining the flexibility of modern machine learning algorithms with an eye towards applications in medicine, biology, and finance.While many applications of machine learning have been driven by impressive advances in complex predictive models, at the same time a need has emerged for models that can extract causal information from massive, unlabeled datasets. Graphical models provide a principled and effective way to uncover this type knowledge from unlabeled data. Although the problem of learning undirected graphs has witnessed a series of remarkable advances over the past decade, directed acyclic graphs (DAGs) that encode directed, potentially causal information, have not benefited from these advances. As a result, there is a pressing need for novel and theoretically sound methods for learning DAGs that can capture complex, asymmetric relationships, reduce model complexity, and most importantly, learn causal relationships for human decision-makers and stakeholders. This project explores a new approach for learning DAGs from data that provides the basis for a general statistical and computational framework, which has been lacking thus far. The technical aims can be divided along three major axes: 1) Developing novel continuous relaxations of the combinatorial optimization problems that arise in structure learning problems, 2) Developing new tools for analyzing the behavior of optimization schemes in highly nonconvex settings, and 3) Theoretical advances in nonparametric causal modeling and its statistical properties.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.

机器学习和人工智能的最新进展归功于它们的成功归功于算法的发展，这些算法学习复杂的关系并了解大量数据集中的复杂现象。这些算法已成功应用于各种应用程序，包括医学，遗传学，机器人技术，市场营销，金融，以及越来越多的社会应用。但是，尽管取得了许多成功，但这些应用程序仍然遭受安全性，透明度，公平性和可解释性问题的困扰。这些实际挑战中的许多可以追溯到有关可解释性，因果关系和错误发现的众所周知的局限性。同时，近年来，在我们对这些实际挑战的理解中，在相对简单的设置中仅具有几个因素和相对简单的模型，已经取得了很大的进步。这项研究旨在整合这些努力，以便为来自高维，复杂数据集的灵活，可解释，因果关系建模提供灵活的框架。 The investigated approach specifically seeks to avoid spurious correlations that commonly appear in complex datasets, while retaining the flexibility of modern machine learning algorithms with an eye towards applications in medicine, biology, and finance.While many applications of machine learning have been driven by impressive advances in complex predictive models, at the same time a need has emerged for models that can extract causal information from massive, unlabeled datasets.图形模型提供了一种有效的有效方法，可以从未标记的数据中揭示此类型的知识。尽管学习无向图的学习问题在过去十年中见证了一系列显着的进步，但编码有针对性的，潜在的因果信息的定向无环形图（DAG）并未从这些进步中受益。结果，对学习可以捕获复杂，不对称关系，降低模型复杂性的新颖和理论上合理的方法的迫切需求，最重要的是，为人类决策者和利益相关者学习因果关系。该项目探索了一种从数据中学习DAG的新方法，该方法为迄今为止缺乏一般统计和计算框架提供了基础。技术目的可以沿三个主要轴划分：1）在结构学习问题中出现的组合优化问题的新型持续放松，2）开发新工具，用于分析高度非convex设置中优化方案的行为，3）3）使用统计学的统计范围及其统计学授予的理论进步。基金会的智力优点和更广泛的影响审查标准。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

IDENTIFIABILITY OF NONPARAMETRIC MIXTURE MODELS AND BAYES OPTIMAL CLUSTERING

DOI：
10.1214/19-aos1887
发表时间：
2020-08-01
期刊：
ANNALS OF STATISTICS
影响因子：
4.5
作者：
Aragam, Bryon;Dan, Chen;Ravikumar, Pradeep
通讯作者：
Ravikumar, Pradeep

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

DOI：
10.48550/arxiv.2205.12548
发表时间：
2022-05
期刊：
影响因子：
0
作者：
Mingkai Deng;Jianyu Wang;Cheng-Ping Hsieh;Yihan Wang-;Han Guo;Tianmin Shu;Meng Song;E. Xing;Zhiting Hu
通讯作者：
Mingkai Deng;Jianyu Wang;Cheng-Ping Hsieh;Yihan Wang-;Han Guo;Tianmin Shu;Meng Song;E. Xing;Zhiting Hu

Learning Latent Causal Graphs Via Mixture Oracles

通过混合预言学习潜在因果图