Advanced Structured Prediction

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On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017, background): “Despite many algorithmic advances to tackle structured prediction problems [4, 35], there have been relatively few papers devoted to its theoretical understanding.”
SparseMAP: Differentiable Sparse Structured Inference (2018, background): “Structured prediction involves the manipulation of discrete, combinatorial structures, e.g. , trees and alignments (Bakır et al., 2007; Smith, 2011; Nowozin et al., 2014).”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “A related field to SRL is structured prediction (SP) (Bakir et al., 2007; Nowozin et al., 2016), which generalizes the tasks of classification and regression to the task of predicting structured objects.”
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain (2017, background): “Among them the LP-based relaxation exhibits the best tradeoff between accuracy and efficiency (particularly on large-scale problems) and is arguably the most investigated approach in recent years [1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14].”