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? <br> <br>\par In order to begin asking and answering these questions, our workshop will bring together researchers from machine learning, computer vision, and natural language processing who are addressing large-scale prediction problems where inference cost is a major bottleneck. To this end, 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. Furthermore, because thecoarse-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. We anticipate that this workshop will lead to concrete 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.