Structured prediction describes problems which involve predicting multiple output variables with expressive and complex interdependencies and constraints. Learning over expressive structures (called structural learning) is usually time-consuming as exploring the structured space can be an intractable problem. The goal ofthis thesis is to present different techniques for structural learning, which learn by decomposing the problem space into simpler and tractable components. We will consider three different settings: fully supervised, unsupervisedand semi-supervised, and discriminative latent variable setting, and present learning techniques for each case.For supervised structural learning, we describe a paradigm called Decomposed Learning (DecL) whichdecomposes the inference procedure during learning into small inference steps using additional application specific information. For unsupervised learning, we propose a family of Expectation Maximization [Dempster et al., 1977] algorithms called Unified Expectation Maximization (UEM) [Samdani et al., 2012a] that covers several seemingly divergent versions of EM e.g. hard EM. To efficiently add domain-specific declarativeconstraints into learning, we use a dual projected subgradient ascent algorithm which naturally decomposesthe task into simpler components. In the discriminative latent variable scenario, we present a supervisedlatent variable model for clustering called the Latent Left-Linking Model (L3M) that can efficiently clusteritems arriving in a streaming order. We decompose the learning process for L3M into small and efficient stochastic gradient descent steps that lead to rapid convergence.
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Algorithms for structural learning with decompositions
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