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，这些系统相互作用，运输货物和原材料，制造为手机和笔记本电脑供电的微小部件，并协助外科医生进行精密的医疗手术。每天，这些系统的操作员必须做出一般决策，例如如何安排工人和送货，以及具体决策，例如装配线上的某个机器人应如何运行。在每种情况下，一个好的决策不仅必须考虑到有关环境的已知信息，而且还必须考虑到未知的情况。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代价高昂的错误。此外，每一个决定都会影响下一个决定，每一步的判断错误和延迟都可能被传播和放大。科学家在做出决策时严重依赖计算机模型来控制未知因素，但在许多情况下，这些模型速度太慢而无用。这项研究将大大减少稳健表示不确定性所需的计算要求，这意味着计算机模型可以以更低的成本更快、更可靠地量化不确定性的影响。在一个对未知事物进行仔细建模的世界中，自动驾驶汽车更安全，基础设施更高效，科学实验信息更丰富。高斯过程是不确定性表示的黄金标准。然而，训练后进行预测的高计算成本限制了它们在贝叶斯优化和强化学习的顺序决策框架中的适用性，在这些框架中，不确定性估计的质量可能会产生巨大的影响。这项研究开发了代数方法，利用硬件设计的进步在这些设置中实现可扩展的高斯过程。这项工作将扩大贝叶斯优化方法对通用目标的适用性，并产生重要的科学影响，例如自动化核磁共振波谱学。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，捕获工程系统的许多可能的未来状态，有效探索可能的状态以及高维状态空间的表示。这些功能是复杂工程系统（例如无人驾驶车辆）中实现自动控制的重要一步，在这些系统中，数据获取成本高昂且安全保证至关重要。总体而言，这项工作将有助于释放概率方法在顺序在线决策中的潜力，同时在教育环境中提供交互式工程演示。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

Fast Adaptation with Linearized Neural Networks

线性神经网络的快速适应

• 发表时间: 2021-03-02
• 期刊: ArXiv
• 作者: Wesley J. Maddox;Shuai Tang;Pablo G. Moreno;A. Wilson;Andreas C. Damianou
• 通讯作者: 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: 2020-01
• 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

Kernel Interpolation for Scalable Online Gaussian Processes

可扩展在线高斯过程的核插值

• DOI: 10.1007/s10115-018-1238-2
• 发表时间: 2021-03-02
• 期刊: Knowledge and Information Systems
• 影响因子: 2.7
• 作者: S. Stanton;Wesley J. Maddox;Ian A. Delbridge;A. Wilson
• 通讯作者: A. Wilson

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition

通过调和核分解的可扩展变分高斯过程

• DOI: 10.18517/ijaseit.14.2.18668
• 发表时间: 2021-06-10
• 期刊: ArXiv
• 作者: Shengyang Sun;Jiaxin Shi;A. Wilson;R. Grosse
• 通讯作者: R. Grosse

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

单纯形上的 SKIing：可扩展高斯过程的全面体格子上的核插值

• 发表时间: 2021-06-12
• 期刊: ArXiv
• 作者: Sanyam Kapoor;Marc Finzi;Ke Ale;er Wang;er;A. Wilson
• 通讯作者: A. Wilson