Collaborative Research: III: Medium: Conditional Transport: Theory, Methods, Computation, and Applications

合作研究：III：媒介：条件传输：理论、方法、计算和应用

• 批准号: 2212419
• 负责人: Xiaoning Qian
• 金额: $ 60万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212419&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; Conditional

Measuring the difference between probability distributions is a fundamental problem in statistics and machine learning (ML). It plays essential roles in many critical ML and artificial intelligence (AI) tasks, such as building deep generative models to synthesize realistic data and training deep reinforcement learning agents. The project’s novelties are 1) establishing Conditional Transport (CT) as a new statistical distance between probability distributions to address several key limitations of existing methods, 2) developing a new distribution-based learning framework with efficient approximate computation algorithms, and 3) applying CT to better solve modern ML/AI problems involving large-scale and high-dimensional data and models. The project’s impacts are 1) advancing distribution-based ML/AI fundamental research, and 2) enabling efficient and robust methods for the ML/AI applications in science, engineering, and bio-medicine, in particular in inverse materials design and multi-omics data analysis. The investigators will integrate the proposed research with training, education, and outreach activities for next-generation workforce development, by developing new ML/AI course materials to better prepare students and researchers at all levels with a diversified educational background, promoting diversity, equity, and inclusion with the emphasis on attracting talents from under-represented groups, with a special emphasis on broadening participation in interdisciplinary computing. This project aims to establish CT and its enabled distribution-based learning framework, which has the paradigm-shift potential to further advance ML/AI research with new models and inference algorithms. In particular, 1) theoretical understanding of CT will provide the foundation of this new learning framework with desired model representation power as well as learning stability. 2) Maximum likelihood estimation, Bayesian inference, and entropy regularized optimal transport will be revisited based on CT, enabling efficient Bayesian computation and optimization taking advantage of modern deep network models and stochastic gradient descent tools. 3) New and improved ML/AI models and inference algorithms will be developed for deep generative modeling, contrastive representation learning, and deep reinforcement learning to advance the state of the art. 4) Inverse materials design and multi-omics data analysis, two real-world applications that require reliable uncertainty quantification for consequent critical decision making, will showcase the advantages of the CT-based methods.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.

衡量概率分布之间的差异是统计和机器学习（ML）的基本问题。它在许多关键的ML和人工智能（AI）任务中扮演着重要角色，例如建立深刻的通用模型来综合现实数据并培训深入的强化学习剂。该项目的新颖性是1）建立条件传输（CT）作为概率分布之间的新统计距离，以解决现有方法的几个关键局限性，2）开发具有有效近似计算算法的新的基于分布的学习框架，以及3）应用CT，应用CT以更好地解决现代ML/AI问题，涉及大型尺度和高度尺度和高度数据和高度数据和模型。该项目的影响是1）基于分配的ML/AI基础研究，以及2）为ML/AI在科学，工程和生物医学中的应用，尤其是在反材料设计和多组学数据分析中，为ML/AI应用提供了有效且可靠的方法。研究人员将通过开发新的ML/AI课程材料来将拟议的研究与培训，教育和外展活动相结合，以更好地为学生和研究人员提供多种教育背景，促进多样性，公平性，并促进多样化的多样性，并重点促进不足的群体的才能吸引不足的群体，并具有专门的宽广的参与，并促进了多样性的教育，并促进了多样性的教育，并促进了多样化的教育，并促进了多样化的教育背景。该项目旨在建立CT及其启用基于分配的学习框架，该框架具有范式偏移的潜力，可以通过新的模型和推理算法进一步推进ML/AI研究。特别是，1）对CT的理论理解将以所需的模型表示能力以及学习稳定性为这个新的学习框架的基础。 2）将基于CT重新访问最大似然估计，贝叶斯推理和熵正规化最佳运输，从而使有效的贝叶斯计算和优化利用现代深层网络模型和随机梯度下降工具。 3）将开发新的和改进的ML/AI模型和推理算法，用于深刻的通用建模，对比度表示学习以及深入的强化学习以推动艺术的状态。 4）倒数材料设计和多词数据分析，这两个现实世界应用需要可靠的不确定性量化来进行关键决策，将展示基于CT的方法的优势。该奖项反映了NSF的法定任务，并被认为是通过使用基金会的知识和更广泛影响的评估来审查Criteria来通过评估来通过评估来获得支持的。