BIGDATA: F: Towards Automating Data Analysis: Interpretable, Interactive, and Scalable Learning via Discrete Probability

随着机器学习（ML）渗透到所有科学和技术领域，不同数据领域的需求、推理问题、资源限制和可靠性引发了一些新的概念和算法挑战。当前限制机器学习充分利用的缺点包括：数据和算法的次优使用；艰苦的手工调整和模型搜索；结果的验证和推广的困难；与人类的互动有限；领域知识的编码；以及缺乏可解释性等等。这些问题的进展有可能影响机器学习在广泛领域的成功采用和使用。基于上述动机，该项目的目标是创建一套新的模型和算法来分析复杂的数据集，特别关注以下三个对下一代机器学习至关重要的因素：(1)可解释性；(2)互动性;(3)自动学习。这一建议背后的首要技术概念是离散概率中的负相关概念。该项目为基于这一概念的一套新工具奠定了理论基础。除了实际影响外，该项目所研究的方法还激发了新的理论问题，并有助于提高对基础数学的兴趣。拟议工作的实际影响有可能在多个方面造福社会。通过合作，pi将评估医疗保健（寻求最终影响患者护理和福祉），系统生物学（帮助研究癌症和糖尿病等）和材料科学（帮助发现更安全，更有效的功能材料）方面的开发方法。该项目还将直接产生教育影响：培训研究生，为各级数据科学课程提供材料，并通过一般讲座以及会议和讲习班（包括针对数据科学女性的讲习班和活动）的重点讲座向社区推广。从技术上讲，pi将开发：(1)用于交互式数据分析的新工具、模型和算法，特别是用于实验设计、信息收集、可解释机器学习、假设检验、性能验证和架构学习；(2)理论分析，如收敛性和复杂性（统计和计算）；(3)所有关键算法和框架的开源实现。

As machine learning (ML) permeates all areas of science and technology, demands in diverse data domains, inference questions, resource limitations and reliability fuel several new conceptual and algorithmic challenges. Examples of current shortcomings that limit the full use of machine learning include suboptimal use of data and algorithms; painstaking hand-tuning and model search; validation of results and difficulties in generalization; limited interactivity with humans; encoding of domain knowledge; and lack of interpretability, among others. Progress on these questions has the potential to impact the successful adoption and use of machine learning in a broad range of fields. With the above motivation, the goal of this project is to create a novel suite of models and algorithms for analyzing complex datasets, with a particular focus on the following three factors crucial for next-generation machine learning: (1) interpretability; (2) interactivity; and (3) automated learning. The overarching technical concept underlying this proposal is the concept of negative dependence in discrete probability. This project lays theoretical foundations for a new set of tools grounded in this concept. Besides practical impacts, the methods to be studied in the project motivate new theoretical questions, and will help increase interest in the underlying mathematics.The practical impact of the proposed work has the potential to benefit society on multiple fronts. Via collaborations, the PIs will evaluate the developed methods in healthcare (seeking to ultimately impact patient care and well-being), systems biology (to help with research on cancer and diabetes, among others), and materials science (to help discover safer, functional materials more efficiently). The project will also directly have educational impact: training of graduate students, providing material for data science courses at all levels, and outreach to the community via general talks as well as focused lectures at conferences and workshops, including workshops and events targeted at women in Data Science. Technically, the PIs will develop: (1) New tools, models, and algorithms for interactive data analysis, especially for experimental design, information collection, interpretable machine learning, hypothesis testing, performance validation, and architecture learning; (2) Theoretical analysis, such as convergence and complexity (statistical and computational); and (3) Open-source implementations of all key algorithms and frameworks.