Introduction to dual composition for inference

By David A. Sontag, Amir Globerson, Tommi S. Jaakkola. 2011

156 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods, Statistical Learning Theory

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Citations in the corpus

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints (2017, methodology): “rder to quantify and we are the ﬁrst to propose methods to reduce bias ampliﬁcation in this context. Lagrangian relaxation and dual decomposition techniques have been widely used in NLP tasks (e.g., (Sontag et al., 2011; Rush and Collins, 2012; Chang and Collins, 2011; Peng et al., 2015)) for dealin”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “One approach is dual decomposition (DD) (Sontag et al., 2011), which solves a problem dual to the LCR objective.”
Dual Decomposition Inference for Graphical Models over Strings (2015, methodology): “It has been widely used for MAP inference in graphical models (Komodakis et al., 2007; Komodakis and Paragios, 2009; Koo et al., 2010; Martins et al., 2011; Sontag et al., 2011; Rush and Collins, 2014).”
Instance Segmentation of Indoor Scenes Using a Coverage Loss (2014, methodology): “When pairwise features are included, this algorithm could be used together with dual decomposition to efficiently perform test-time inference [25].”
Structured Learning and Prediction in Computer Vision (2011, methodology): “[141] provides a detailed introduction to dual decomposition and dual ascent methods for inference.”
Predicting Failures of Vision Systems (2014, background): “Predicting failures: Optimization algorithms can often detect when they have found the global optimum [52] or can”
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference (2012, background): “, 2006), expectation propagation (Minka, 2001), and dual decomposition (Wolsey, 1998; Sontag et al., 2010).”
Advanced Structured Prediction (2014, methodology): “Equivalently, it can be understood as dual decomposition (or Lagrangian relaxation) of VCSP (Johnson et al., 2007; Komodakis et al., 2011; Sontag et al., 2011).”
AD3: alternating directions dual decomposition for MAP inference in graphical models (2015, background): “A slightly different formulation is presented by Sontag et al. (2011) which yields a subgradient algorithm with no projection.”
Similarity Constrained Latent Support Vector Machine: An Application to Weakly Supervised Action Classification (2012, methodology): “We use dual decomposition [9] to approximately solve this inference.”