Workshop: Student Scholarship Program for the International Conference on Machine Learning (ICML 2001)

这是一笔资助学生参加特别海报会议的资金，作为第18届机器学习国际会议（ICML'2001）的一部分，pi将于6月28日至7月1日在马萨诸塞州威廉斯敦举行（pi是会议的项目联合主席）。这是机器学习研究的顶级会议。pi的目标是将学生整合到他们的研究子社区中，并为他们提供一个论坛，与该领域的主要研究人员讨论他们的博士研究。为此，将在会议的第一个晚上组织海报会议，每个被赞助的学生将有一张海报。为了保证足够的流量，pi将要求每个项目委员会成员提前在网上报名，以学生的摘要为基础，在当晚看到至少5张海报。学生还将获得一页摘要提交给会议记录。正如提案中详述的那样，pi们认为这个计划比其他会议组织的更传统的博士研讨会有好处。pi将根据提交的摘要和个人简历以及学生导师的推荐信，从申请人中选择奖学金。他们希望获得30-40个奖项，每个奖项约500美元。辅导工作将分配给项目委员会的成员。海报会议将为学生在研究的关键时刻提供宝贵的外部视角。这是一种廉价但非常有效的方法，可以鼓励年轻和即将进入机器学习领域的研究人员。

This is funding to subsidize expenses of student participants in a special poster session the PIs are organizing as part of the 18th annual International Conference on Machine Learning (ICML'2001), which will be held June 28-July 1 in Williamstown, MA (the PIs are the Program Co-Chairs of the conference). This is the premier conference for machine learning research. The PIs' goals are to integrate students into their research sub-communities and to give them a forum to talk to the leading researchers in the field about their PhD research. To this end, a poster session will be organized on one of the first nights of the conference where each sponsored student will have a poster. To ensure sufficient traffic, the PIs will require each program committee member to sign up ahead of time on the Web to see at least 5 posters that evening, on the basis of the students' abstracts. The students will also get a one page abstract submission to the conference proceedings. The PIs feel this plan has benefits over the more traditional PhD workshops organized by other conferences, as detailed in the proposal. The PIs will choose among the applicants for the scholarships, on the basis of a submitted abstract and vita plus a letter of recommendation from the student's advisor. They expect to make 30-40 awards of approximately $500 apiece. The mentoring work will be distributed among the members of the program committee. The poster session will provide students with invaluable exposure to outside perspectives on their work at a critical time in their research. This is an inexpensive yet highly effective means of encouraging young and upcoming researchers in machine learning.