Statistical decision making on non-euclidean spaces

非欧空间的统计决策

• 批准号: 2420122
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2420122
• 关键词: Statistical; decision; making; non; euclidean

Context: With the advent of the big data era, data has never been available in such volume and velocity. In parallel, machine learning models are now entering cost sensitive and safety sensitive applications ranging from drug discovery, autonomous driving or medical decision making among others. Having well calibrated measures of uncertainty and risk while decisions are made is paramount for the success and adoption of machine learning tools in these settings. AIMS: The aim of this DPhil project is to exploit the geometry induced by large datasets to improve uncertainty quantification in machine learning models in safety critical tasks. Alignment to EPSRC strategies: This DPhil is well aligned with the Artificial intelligence technologies area of EPSCR's research. Novelty of the research methodology:This research will establish cross-disciplinary links between differential geometry, random manifolds and bayesian machine learning for novel insights. Companies or collaborators involved: This DPhil project is supported by the AWS Studentship in collaboration with the Oxford-Singapore Human Machine Collaboration (HMC) initiative.

背景：随着大数据时代的到来，数据的数量和速度前所未有。与此同时，机器学习模型现在正在进入成本敏感和安全敏感的应用，包括药物发现、自动驾驶或医疗决策等。在做出决策时对不确定性和风险进行良好校准对于机器学习工具在这些环境中的成功和采用至关重要。 目标：这个 DPhil 项目的目的是利用大型数据集引起的几何形状来改进安全关键任务中机器学习模型的不确定性量化。与 EPSRC 战略保持一致：该博士学位与 EPSRC 研究的人工智能技术领域非常一致。研究方法的新颖性：这项研究将在微分几何、随机流形和贝叶斯机器学习之间建立跨学科联系，以获得新颖的见解。参与的公司或合作者：该 DPhil 项目由 AWS Studentship 与牛津-新加坡人机合作 (HMC) 计划合作支持。