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，制造手机和笔记本电脑的微型组件，并协助外科医生进行精细的医疗手术。每天，这些系统的操作员都必须做出一般性的决定，比如如何安排工人和交付，以及具体的决定，比如装配线上的某个机器人应该如何工作。在每一种情况下，一个好的决策不仅要考虑到环境的已知情况，还要考虑到环境的未知情况。有时候应该收集更多的信息，有时候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误都会传播和扩大。科学家们在做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，模型太慢而无法使用。这项研究将大大降低不确定性的鲁棒表示所需的计算要求，这意味着计算机模型可以更快，更可靠地量化不确定性的影响，成本更低。在一个未知数被仔细建模的世界里，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，在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，其中不确定性估计的质量可能会产生巨大的影响。本研究开发的代数方法，利用这些设置中的可扩展高斯过程的硬件设计的进步。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NMR光谱。这项研究还将使基于模型的强化学习中的假设更加现实，以捕获工程系统的许多可能的未来状态，有效地探索可能的状态，并表示高维状态空间。这些功能是实现复杂工程系统自动控制的重要一步，例如无人驾驶车辆，在这些系统中，获取数据的成本很高，安全保障至关重要。总的来说，这项工作将有助于释放概率方法的潜力，为顺序在线决策，同时提供互动的工程示范，在教育settings.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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

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

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