Robust and Efficient Structured Prediction

稳健高效的结构化预测

• 批准号: RGPIN-2017-06936
• 负责人: LacosteJulien, Simon
• 金额: $ 1.82万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656301
• 关键词: Robust; Efficient; Structured; Prediction

Machine learning is widely used in science and technology with deployed tools like automatic spam classification for emails or face detectors in digital cameras. Yet today's partial solutions to the fundamental problem of structured prediction, that is, learning to make multiple interrelated predictions (e.g. predicting the sequence of translated words for an input sentence in machine translation), lag far behind those available for binary classification in term of accuracy and scalability. Progress has been fragmented, and the theory is almost nonexistent, preventing the widespread adoption of the technology to new areas. The key challenge in structured prediction is the combinatorial explosion of choices for the output, requiring a radical new marriage of computation and statistics.******The objective of this research program is to elaborate a general theoretical and algorithmic framework for robust and efficient structured prediction. The goal is to bring structured prediction to a level of maturity and usability similar to that of binary classification in modern machine learning. I plan to attack this problem through the tools of statistical consistency of surrogate losses. Our theoretical work will provide the groundwork to build new robust structured prediction models that can handle weak supervision. Radically more efficient structured prediction machines will be obtained through our algorithmic work on advanced convex and combinatorial optimization. The applicability of the framework will be demonstrated through applications in computer vision, natural language processing and computational biology.******More specifically, my research program will address the following four scientific challenges:***1) Provide a unified theoretical analysis of structured prediction models.***2) Propose novel structured prediction models that enjoy good theoretical properties, are tractable, and address the particularities of the field such as weak supervision.***3) Design efficient algorithms that solve the underlying large-scale convex or non-convex optimization problems.***4) Demonstrate the applicability of the framework in several application areas.******Breakthrough progress on structured prediction will have high impact on statistical machine learning research, notably by providing a new solution to the open problem of making robust interrelated predictions. Moreover, the developed methodology will directly impact numerous application areas in science and technology by enabling the widespread adoption of advanced structured prediction.

机器学习在科学和技术领域得到了广泛的应用，例如电子邮件的自动垃圾邮件分类或数码相机中的人脸检测器。然而，今天对结构化预测基本问题的部分解决方案，即学习进行多个相互关联的预测（例如，在机器翻译中预测输入句子的翻译单词序列），在准确性和可扩展性方面远远落后于可用于二进制分类的解决方案。进展是分散的，理论几乎不存在，阻止了该技术在新领域的广泛采用。结构化预测的关键挑战是输出选择的组合爆炸，需要计算和统计的全新结合。本研究计划的目的是阐述一个通用的理论和算法框架，强大和有效的结构化预测。我们的目标是使结构化预测达到与现代机器学习中的二进制分类相似的成熟度和可用性水平。我计划通过替代损失的统计一致性工具来解决这个问题。我们的理论工作将为建立新的鲁棒结构化预测模型提供基础，这些模型可以处理弱监督。从根本上更有效的结构化预测机器将通过我们的算法工作先进的凸和组合优化。该框架的适用性将通过在计算机视觉，自然语言处理和计算生物学中的应用得到证明。更具体地说，我的研究计划将解决以下四个科学挑战：*1）提供结构化预测模型的统一理论分析。2)提出新颖的结构化预测模型，这些模型具有良好的理论特性，易于处理，并解决了该领域的特殊性，如弱监督。3)设计高效的算法来解决潜在的大规模凸或非凸优化问题。* 4)展示该框架在多个应用领域的适用性。**结构化预测的突破性进展将对统计机器学习研究产生重大影响，特别是通过提供一种新的解决方案来解决做出强大的相互关联的预测的开放问题。此外，所开发的方法将直接影响科学和技术的许多应用领域，使先进的结构化预测的广泛采用。