RI: Small: Scalable Online Learning with Gaussian Processes

RI：小型：使用高斯过程的可扩展在线学习

• 批准号: 1910266
• 负责人: Andrew Wilson
• 金额: $ 39.91万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910266&HistoricalAwards=false
• 关键词: RI; Small; Scalable; Online; Learning

The modern world is filled with highly complex systems interacting to transport goods and raw materials, to manufacture the tiny components that power phones and laptops, and to assist surgeons in delicate medical procedures. Every day, the operators of these systems must make general decisions like how to schedule workers and deliveries, and specific decisions like how a certain robot in an assembly line should function. In each case, a good decision must account not only for what is known about the environment, but also what is unknown. Sometimes more information should be gathered, and sometimes action must be taken to avoid unlikely but costly mistakes. Moreover, every decision affects the next, and errors and delays in judgement at each step can be propagated and amplified. Scientists rely heavily on computer models to control for unknowns when making decisions, but in many situations the models are simply too slow to be useful. This research will greatly reduce the computational requirements needed for a robust representation of uncertainty, meaning computer models can quantify the effect of uncertainty more quickly and reliably, at a lower cost. In a world where unknowns are carefully modeled, autonomous vehicles are safer, infrastructure is more efficient, and scientific experiments are more informative.Gaussian processes are a gold standard for uncertainty representation. However, the high computational cost of making predictions, after training, has limited their applicability in the sequential decision making frameworks for Bayesian optimization and reinforcement learning, where the quality of uncertainty estimates can have enormous impact. This research develops algebraic methods that exploit advances in hardware design for scalable Gaussian processes in these settings. This work will broaden the applicability of Bayesian optimization methods to general purpose objectives, with crucial scientific impacts such as automating NMR spectroscopy. This research will also enable more realistic assumptions in model-based reinforcement learning, to capture many possible future states of an engineering system, efficient exploration of possible states, and representation of high dimensional state spaces. These features are an important step towards automatic control in complicated engineering systems, such as unmanned vehicles, where data is costly to acquire and safety guarantees are critical. Overall this work will help unlock the potential of probabilistic methods for sequential online decision making, while providing interactive engineering demonstrations in educational settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代世界充满了高度复杂的系统，这些系统与运输商品和原材料相互作用，以制造为手机和笔记本电脑供电的微小组件，并协助外科医生进行精致的医疗程序。每天，这些系统的运营商都必须做出一般决策，例如如何安排工人和交货，以及诸如装配线中的某个机器人应该如何运行的具体决策。在每种情况下，一个好的决定不仅必须说明对环境的了解，而且还必须说明未知的内容。有时应该收集更多信息，有时必须采取行动以避免不太可能但昂贵的错误。此外，每个决定都会影响下一个决定，每个步骤的错误和判断都可以传播和放大。科学家在做出决策时很大程度上依赖计算机模型来控制未知数，但是在许多情况下，模型太慢而无法有用。这项研究将大大减少不确定性的强大表示所需的计算要求，这意味着计算机模型可以以较低的成本更快，可靠地量化不确定性的效果。在仔细建模未知数的世界中，自动驾驶汽车更安全，基础设施更有效，科学实验更具信息性。Gaussian过程是不确定性表示的黄金标准。但是，在训练之后，进行预测的高计算成本限制了其在贝叶斯优化和强化学习的顺序决策框架中的适用性，在这种框架中，不确定性估计的质量可能会产生巨大影响。这项研究开发了代数方法，可以利用硬件设计的进步来为这些设置中的可扩展高斯流程。这项工作将扩大贝叶斯优化方法对通用目标的适用性，并具有至关重要的科学影响，例如自动化NMR光谱。这项研究还将在基于模型的强化学习中实现更现实的假设，以捕获工程系统的许多可能未来状态，对可能状态的有效探索以及高维状态空间的代表。这些功能是在复杂的工程系统（例如无人车辆）中自动控制的重要一步，那里的数据成本高昂，安全保证至关重要。总的来说，这项工作将有助于释放概率方法进行顺序在线决策的潜力，同时在教育环境中提供互动工程示范。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。

项目成果

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition

• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Shengyang Sun-;Jiaxin Shi;A. Wilson;R. Grosse

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes

• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Sanyam Kapoor;Marc Finzi;Ke Alexander Wang;A. Wilson

Kernel Interpolation for Scalable Online Gaussian Processes

• 发表时间: 2021-03
• 期刊: Knowledge and Information Systems
• 影响因子: 2.7
• 作者: S. Stanton;Wesley J. Maddox;Ian A. Delbridge;A. Wilson

Fast Adaptation with Linearized Neural Networks

• 发表时间: 2021-03
• 期刊: ArXiv
• 作者: Wesley J. Maddox;Shuai Tang;Pablo G. Moreno;A. Wilson;Andreas C. Damianou

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

BoTorch：高效蒙特卡罗贝叶斯优化框架

• 发表时间: 2020
• 期刊: Advances in Neural Information Processing Systems (NeurIPS
• 作者: Balandat, M;Karrer, B;Jiang, D;Daulton, S;Letham, B;Wilson, A.G.;Bakshy, E
• 通讯作者: Bakshy, E