CAREER: Scalable Machine Learning for Astrostatistics

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目正在设计新的数据结构和有效的算法，用于将现代机器学习技术扩展到大规模数据集，并将其应用于最近的天空调查，以解决天文学中的核心问题。长期目标是通过专注于关键的计算原语，并通过创建教育计划让后代也能这样做，来扩展所有最好的机器学习技术。在短期内，该项目正在加速奇异值分解（SVD），这是机器学习中许多最先进方法的关键计算瓶颈。除了经典的主成分分析，我们认为我们的想法的应用内核岭回归，图形模型推理，最大方差展开，每个代表一个更大的类（核化，图形和凸模型）。与领先的天体物理学家合作，我们在一个基本的天文数据分析问题中验证了其中的每一个：分别估计到物体的距离，不同目录中物体的交叉匹配，以及发现新类型的物体。关键的洞察力是使用一种新的数据结构，称为余弦树，它基于向量的相互正交性进行分区，使用基于距离的几何问题的成功想法的类比，以实现新的蒙特卡洛采样技术。 初步结果表明，在中等规模的问题中，与精确SVD相比，速度提高了20，000倍，且具有用户可指定的高近似精度。这项工作的更广泛影响是利用先进的数据分析技术的变革性能力，以释放当前和未来万亿和千万亿级数据集中的科学，工程和商业的潜在洞察力。关于这些目标，该项目的教育目标是将现实世界的数据分析和跨学科思维深度整合到传统的计算程序中。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project is designing new data structures and efficient algorithms for scaling modern machine learning techniques to massive datasets, and their application to recent sky surveys to solve central problems in astronomy. The long term goal is to scale up all the best machine learning techniques, by focusing on key computational primitives, and by creating the educational initiatives to allow future generations to do the same.In the shorter term, the project is accelerating the singular value decomposition (SVD), the key computational bottleneck in a number of state-of-the-art methods in machine learning (and well beyond). In addition to the classic principal component analysis, we consider the application of our ideas to kernel ridge regression, graphical model inference, and maximum variance unfolding, each representing a larger class (kernelized, graphical, and convex models). Working with leading astrophysicist collaborators, we validate each of these in a fundamental astronomical data analysis problem: respectively, estimation of the distances to objects, cross-matching of objects in different catalogs, and discovery of new types of objects. The key insight is the use of a new data structure called a cosine tree, which partitions vectors based on their mutual orthogonality, using analogies of successful ideas for distance-based geometric problems to enable a new Monte Carlo sampling technique. Preliminary results demonstrate as much as 20,000 times speedup over exact SVD in moderate-sized problems with user-specifiable high approximation accuracy.The broader impact of the work is the transformative ability to utilize the advanced data analysis techniques to unlock the potential insights across science, engineering, and business lying within the tera- and peta-scale datasets of the present and future. Apropos these goals, the project educational goals are deep integration of real-world data analysis, and cross-disciplinary thinking into traditional computing programs.