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

这笔资金用于资助学生参加特别海报会议的费用，该会议是第18届国际机器学习会议(ICML‘2001)的一部分，该会议将于6月28日至7月1日在马萨诸塞州威廉斯敦举行(私人投资者是会议的项目联合主席)。这是机器学习研究的首要会议。PI的目标是将学生整合到他们的研究子社区中，并给他们一个论坛，让他们与该领域的领先研究人员谈论他们的博士研究。为此，将在会议的第一个晚上组织一次海报会议，每个受赞助的学生都将有一张海报。为了确保足够的流量，PI将要求每个项目委员会成员提前在Web上注册，以根据学生的摘要在当晚至少看到5张海报。学生还将获得一份一页的会议记录摘要。PI们认为，与提案中详细说明的其他会议组织的更传统的博士研讨会相比，这一计划具有优势。PIS将根据提交的摘要和简历以及学生导师的推荐信从奖学金申请者中进行选择。他们预计将获得30-40个奖项，每个奖项约为500美元。辅导工作将在项目委员会成员之间分配。海报课程将为学生提供宝贵的机会，让他们在研究的关键时刻接触到外界对他们工作的看法。这是一种廉价但非常有效的方法，可以鼓励年轻的和未来的机器学习研究人员。

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.