Apologies for multiple copies of this announcement. ######################################################################
CALL FOR PAPERS Coarse-to-Fine Learning and Inference a workshop in conjunction with 24th Annual Conference on Neural Information Processing Systems (NIPS 2010) December 10, 2010 Whistler, BC, Canada http://learning.cis.upenn.edu/coarse2fine/ Deadline for Submissions: Friday, October 29, 2010 Notification of Decision: Monday, November 8, 2010 ##################################################################### Overview The bottleneck in many complex prediction problems is the prohibitive cost of inference or search at test time. Examples include structured problems such as object detection and segmentation, natural language parsing and translation, as well as standard classification with kernelized or costly features or a very large number of classes. These problems present a fundamental trade-off between approximation error (bias) and inference or search error due to computational constraints as we consider models of increasing complexity. This trade-off is much less understood than the traditional approximation/estimation (bias/variance) trade-off but is constantly encountered in machine learning applications. The primary aim of this workshop is to formally explore this trade-off and to unify a variety of recent approaches, which can be broadly described as "coarse-to-fine" methods, that explicitly learn to control this trade-off. Unlike approximate inference algorithms, coarse-to-fine methods typically involve exact inference in a coarsened or reduced output space that is then iteratively refined. They have been used with great success in specific applications in computer vision (e.g., face detection) and natural language processing (e.g., parsing, machine translation). However, coarse-to-fine methods have not been studied and formalized as a general machine learning problem. Thus many natural theoretical and empirical questions have remained un-posed; e.g., when will such methods succeed, what is the fundamental theory linking these applications, and what formal guarantees can be found? A significant portion of the workshop will be given over to discussion, in the form of two organized panel discussions and a small poster session. We have taken care to invite speakers who come from each of the research areas mentioned above, and we intend to similarly ensure that the panels are comprised of speakers from multiple communities. We anticipate that this workshop will lead to new research directions in the analysis and development of coarse-to-fine and other methods that address the bias/computation trade-off, including the establishment of several benchmark problems to allow easier entry by researchers who are not domain experts into this area. Call for Participation We invite submission of workshop papers that discuss ongoing or completed work in machine learning, computer vision, and natural language processing and addressing large-scale prediction problems where inference cost is a major bottleneck. Furthermore, because the "coarse-to-fine" label is broadly interpreted across many different fields, we also invite any submission that involves learning to address the bias/computation trade-off or that provides new theoretical insight into this problem. A workshop paper should be no more than six pages in the standard NIPS format. Authorship should not be blind. Please submit a paper by emailing it in Postscript or PDF format to coarse2finenips2...@gmail.com. We anticipate accepting six such papers for poster presentations, some of which will also receive an oral presentation. Please only submit an article if at least one of the authors will be able to attend the workshop and present the work. * Please use NIPS template and style files. No more than 6 pages, authorship not blind. * Submit to coarse2finenips2...@gmail.com by October 29. Important Dates: * Friday, October 29 -- Paper submission deadline * Monday, November 8 -- Notification of acceptance Organizers: Ben Taskar tas...@cis.upenn.edu University of Pennsylvania David Weiss djwe...@cis.upenn.edu University of Pennsylvania Ben Sapp bens...@cis.upenn.edu University of Pennsylvania Slav Petrov pet...@cs.berkeley.edu Google Research, New York _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
