Computational Challenges in Machine Learning

机器学习中的计算挑战

• 批准号: 1639630
• 负责人: Richard Karp
• 金额: $ 2万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1639630&HistoricalAwards=false
• 关键词: Computational; Challenges; Machine; Learning

The goal of this workshop is to advance the algorithmic frontier of machine learning. Target areas include Bayesian statistics, in which many of the core algorithmic problems bear similarity to problems that have been studied intensively in the theoretical computer science community; and large-scale optimization, in which a host of interesting challenges arise at the interface of theory and practical deployment.The workshop will bring together researchers in algorithms, statistics, mathematics and artificial intelligence. It will be open to all potential participants, and the workshop findings (including videotapes of presentations) will be distributed to the public for comments and engagement. The organizers will encourage students to attend the workshop, and will actively recruit scientists from a diversity of backgrounds to contribute to a wide range of algorithmic topics.

本次研讨会的目标是推进机器学习的算法前沿。目标领域包括贝叶斯统计学，其中许多核心算法问题与理论计算机科学界深入研究的问题具有相似性；以及大规模优化，其中在理论和实际部署的界面上出现了许多有趣的挑战。研讨会将汇集算法、统计学、数学和人工智能的研究人员。它将向所有潜在的参与者开放，研讨会的成果(包括介绍的录像带)将分发给公众，以征求意见和参与。组织者将鼓励学生参加研讨会，并将积极招募来自不同背景的科学家为广泛的算法主题做出贡献。