Computational Foundations of Machine Learning in the Era of Big Data

机器学习（ML）是一个开发可以通过学习和经验来改进自己的软件的领域，它在很大程度上是由历史数据的可用性，以及开发高效、可扩展的算法和支持理论的需求驱动的。相反，机器学习在科学、工程和商业领域的成功，以及技术创新，导致了对大数据收集的前所未有的增长和热情，从而重新定义了计算效率并引入了系统解决方案。例如，最近Deepmind的AlphaGo系统击败了顶尖的人类围棋选手，需要1900个cpu和280个gpu来进行计算。如何在这个庞大的分布式集群中平衡计算和通信，而不影响系统吞吐量或正确性？另一方面，开发移动应用的小型初创公司可能负担不起谷歌那样的计算能力，因此通常不得不转向原始解决方案。如何为机器学习构建一个算法框架，提供“旋钮”来调整计算负载，同时对准确性有明确的、可控的损失？因此，在大数据时代满足如此多样化的计算需求对ML领域来说是一个巨大的挑战。

Machine learning (ML), a field that develops software that can improve itself through learning and experience, has been largely driven by the availability of historical data, and by the need to develop efficient and scalable algorithms and supporting theories. Conversely, the success of ML in science, engineering, and commerce, along with technological innovations, has led to an unprecedented growth and enthusiasm in big data collection, thereby redefining computational efficiency and inviting system solutions. For example, the recent AlphaGo system of Deepmind that beats top human Go players needed 1900 CPUs and 280 GPUs to carry out the computation. How to balance computation with communication in this vast distributed cluster, without compromising system throughput or correctness? On the other hand, a small startup developing a mobile app may not afford the same computational power as Google, hence often has to turn into primitive solutions. How to build an algorithmic framework for ML that provides ''knobs'' to adjust the computational load, with explicit, controllable loss on the accuracy? Meeting such diverse computational needs in the big data era has thus been a grand challenge for the ML field.