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February 5, Sunday
14:00 – 15:00

Structured Prediction is a branch of Machine Learning which is concerned with prediction of complex outputs, such as sequences, trees and graphs, with applications in Natural Language Processing, Computer Vision and Computational Biology. Most structured prediction inference problems are intractable, and as a result many algorithms sacrifice model expressivity (i.e. the kinds of informations that can be taken into account when making predictions) in favor of polynomial-time exact inference. I advocate a different framework, in which exact inference is sacrificed in favor of expressive models. Instead of being trained to optimize a global objective function, the models are trained to make a sequence of greedy locally-optimal decisions, while taking easier choices before harder ones, and relaying on earlier predictions do disambiguate later ones. The resulting algorithms are very fast while remaining competitive in terms of prediction accuracy.