Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models provide a clean separation between modeling and inference, learning these models with approximate inference is not well understood. Furthermore, even if a good model is learned, predictions are often inaccurate due to approximations. In this work, instead of performing inference over a graphical model, we instead consider the inference procedure as a composition of predictors. Specifically, we focus on message-passing algorithms, such as Belief Propagation, and show how they can be viewed as procedures that sequentially predict label distributions at each node over a graph. Given labeled graphs, we can then train the sequence of predictors to output the correct labelings. The result no longer corresponds to a graphical model but simply defines an inference procedure, with strong theoretical properties, that can be used to classify new graphs. We demonstrate the scalability and efficacy of our approach on 3D point cloud classification and 3D surface estimation from single images. 1.

Motivated by the large number of languages (seven) and the short development time (two months) of the 2009 CoNLL shared task, we exploited latent variables to avoid the costly process of hand-crafted feature engineering, allowing the latent variables to induce features from the data. We took a pre-existing generative latent variable model of joint syntacticsemantic dependency parsing, developed for English, and applied it to six new languages with minimal adjustments. The parser’s robustness across languages indicates that this parser has a very general feature set. The parser’s high performance indicates that its latent variables succeeded in inducing effective features. This system was ranked third overall with a macro averaged F1 score of 82.14%, only 0.5 % worse than the best system. 1

Motivated by the large number of languages (seven) and the short development time (two months) of the 2009 CoNLL shared task, we exploited latent variables to avoid the costly process of hand-crafted feature engineering, allowing the latent variables to induce features from the data. We took a pre-existing generative latent variable model of joint syntacticsemantic dependency parsing, developed for English, and applied it to six new languages with minimal adjustments. The parser’s robustness across languages indicates that this parser has a very general feature set. The parser’s high performance indicates that its latent variables succeeded in inducing effective features. This system was ranked third overall with a macro averaged F1 score of 82.14%, only 0.5 % worse than the best system.

Statistical machine learning has become an integral technology for solving many informatics applications. In particular, corpus-based statistical techniques have emerged as the dominant paradigm for core natural language processing (NLP) tasks such as parsing, machine translation, and information extraction, amongst others. However, while supervised machine learning is well understood, its successful application to practical scenarios is predicated on obtaining large annotated corpora and performing significant feature engineering, both notably expensive undertakings. Interactive learning protocols offer one promising solution for reducing these costs by allowing the learner and domain expert to interact during learning in an effort to both reduce sample complexity and improve system performance. By specifying a method where the learner may request targeted information, the domain expert is focused on providing the most useful information. This work formalizes a general framework for interactive learning and examines two interactive learning protocols with particular attention to natural language scenarios. We first examine active learning for structured output spaces, the scenario where there are multiple predictions which must be composed into a structurally coherent global prediction. Secondly, we examine active learning for pipeline models, where a complex prediction is decomposed into a sequence of predictions