1 Introduction to Dual Decomposition for Inference

By David A. Sontag. 2010

Subfields: Optimization & Training, Mathematical Optimization

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Citations in the corpus

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints (2017, methodology): “, (Sontag et al., 2011; Rush and Collins, 2012; Chang and Collins, 2011; Peng et al., 2015)) for dealing with instance-level constraints.”
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing (2012, background): “Recent work (Jojic et al., 2010; Martins et al., 2011) has considered alternative optimization methods; see also Sontag et al. (2010) for a description of alternative algorithms, in particular dual coordinate descent.”
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs (2017, methodology): “This was also found to be important for message-passing to achieve accurate results and is reminiscent of techniques used in dual decomposition ( 41 ).”
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.”
Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation (2018, methodology): “To optimize them, we adopt Block Coordinate Descent (BCD) (Sontag, Globerson, and Jaakkola 2011), which iteratively optimizes the parameters of the two deep models θS , θT and G in a recurrent process.”
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).”
Exploring Compositional High Order Pattern Potentials for Structured Output Learning (2013, methodology): “Also related is the work of [3], which uses a generative framework to model labels and images.”
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.”
Structured Learning and Prediction in Computer Vision (2011, methodology): “[141] provides a detailed introduction to dual decomposition and dual ascent methods for inference.”
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).”
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.”
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].”
Predicting Failures of Vision Systems (2014, background): “tect when they have found the global optimum [52] or can”