RI: Small: Theory and Algorithms for Learning Perturbation Models

RI：小：学习扰动模型的理论和算法

• 批准号: 1524427
• 负责人: Tommi Jaakkola
• 金额: $ 40.71万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1524427&HistoricalAwards=false
• 关键词: RI; Small; Theory; Algorithms; Learning

Machine learning concerns designing and understanding computer programs that learn from experience. Modern complex settings (for example natural language) require the use of flexible probability models that permit one to entertain large numbers of possible hypotheses (semantics) underlying the observations (sentences). In such models likely structures (parse trees) are guided by functions that assess the suitability of structures by breaking them into smaller pieces. Richer models require larger subsets making it challenging to efficiently explore large sets of possible hypotheses. This project takes a fresh look at structured modeling by developing a new paradigm for modeling by combining randomization of parameters and combinatorial optimization. The combination provides a mechanism for inducing complex distributions over structures yet explicitly maintaining easy generation of likely structures. We pursue a comprehensive plan to understand, extend, and design these perturbation models towards the end goal of solving significant cross-cutting applied problems in natural language processing such as parsing or structured recommender tasks such as paraphrasing. Beyond modeling, the proposed work has the potential to merge tools and techniques across areas from theoretical computer science (stability, tractability), combinatorial optimization (relaxations, certificates), to probability (sampling from convex bodies). The tools developed will be broadly useful across prominent areas, from computer vision, natural language processing, to medical informatics and computational biology. The proposed work by its very nature compels strong collaborative relationships across disciplinary boundaries, from theory to applications. The PI will actively pursue these opportunities. All the software produced in this project will be open-sourced, and made available for download. The PI will also engage in outreach activities that enable high school students to participate.

机器学习涉及设计和理解从经验中学习的计算机程序。现代复杂的环境（例如自然语言）需要使用灵活的概率模型，允许人们接受大量可能的假设（语义）作为观察（句子）的基础。在这样的模型中，可能的结构（解析树）由函数引导，该函数通过将结构分解为更小的片段来评估结构的适用性。更丰富的模型需要更大的子集，这使得有效地探索大量可能的假设成为一项挑战。这个项目需要重新审视结构化建模，通过结合参数的随机化和组合优化来开发一种新的建模范式。该组合提供了一种机制，用于在结构上诱导复杂分布，但明确地保持容易生成可能的结构。我们追求一个全面的计划来理解，扩展和设计这些扰动模型，最终目标是解决自然语言处理中的重要交叉应用问题，如解析或结构化推荐任务，如释义。除了建模，拟议的工作有可能合并跨领域的工具和技术，从理论计算机科学（稳定性，易处理性），组合优化（松弛，证书），概率（从凸体采样）。所开发的工具将广泛适用于从计算机视觉、自然语言处理到医学信息学和计算生物学等重要领域。拟议的工作，其本身的性质迫使强大的跨学科边界的合作关系，从理论到应用。PI将积极寻求这些机会。该项目中产生的所有软件都将是开源的，并可供下载。PI还将参与外展活动，使高中学生参与。