Results 1  10
of
114
Learning Efficiently with Approximate Inference via Dual Losses
"... Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation. Previous approaches for learning for structured prediction (e.g., cuttingplane, subgradient methods, perceptron) repeat ..."
Abstract

Cited by 38 (7 self)
 Add to MetaCart
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation. Previous approaches for learning for structured prediction (e.g., cuttingplane, subgradient methods, perceptron
NormProduct Belief Propagation: PrimalDual MessagePassing for Approximate Inference
, 2008
"... Inference problems in graphical models can be represented as a constrained optimization of a free energy function. In this paper we treat both forms of probabilistic inference, estimating marginal probabilities of the joint distribution and finding the most probable assignment, through a unified me ..."
Abstract

Cited by 53 (11 self)
 Add to MetaCart
Inference problems in graphical models can be represented as a constrained optimization of a free energy function. In this paper we treat both forms of probabilistic inference, estimating marginal probabilities of the joint distribution and finding the most probable assignment, through a unified
Accelerated dual decomposition for MAP inference
 In ICML
, 2010
"... Approximate MAP inference in graphical models is an important and challenging problem for many domains including computer vision, computational biology and natural language understanding. Current stateoftheart approaches employ convex relaxations of these problems as surrogate objectives, but ..."
Abstract

Cited by 37 (1 self)
 Add to MetaCart
Approximate MAP inference in graphical models is an important and challenging problem for many domains including computer vision, computational biology and natural language understanding. Current stateoftheart approaches employ convex relaxations of these problems as surrogate objectives
Approximate inference using unimodular graphs in dual decomposition
"... We use the property of unimodular functions to perform approximate inference with dual decomposition in binary labeled graphs. Exact inference is possible for a subclass of binary labeled graphs that have unimodular functions. We call such graphs unimodular graphs. These are graphs where the submodu ..."
Abstract
 Add to MetaCart
We use the property of unimodular functions to perform approximate inference with dual decomposition in binary labeled graphs. Exact inference is possible for a subclass of binary labeled graphs that have unimodular functions. We call such graphs unimodular graphs. These are graphs where
Approximate inference and stochastic optimal control
, 2013
"... We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic opti ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic
Augmenting Dual Decomposition for MAP Inference
"... In this paper, we propose combining augmented Lagrangian optimization with the dual decomposition method to obtain a fast algorithm for approximate MAP (maximum a posteriori) inference on factor graphs. We also show how the proposed algorithm can efficiently handle problems with (possibly global) st ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
In this paper, we propose combining augmented Lagrangian optimization with the dual decomposition method to obtain a fast algorithm for approximate MAP (maximum a posteriori) inference on factor graphs. We also show how the proposed algorithm can efficiently handle problems with (possibly global
On stochastic optimal control and reinforcement learning by approximate inference
 In Proc. of Robotics: Science and Systems VIII (R:SS
, 2012
"... We present a reformulation of the stochastic optimal control problem in terms of KL divergence minimisation, not only providing a unifying perspective of previous approaches in this area, but also demonstrating that the formalism leads to novel practical approaches to the control problem. Specifical ..."
Abstract

Cited by 22 (7 self)
 Add to MetaCart
. Specifically, a natural relaxation of the dual formulation gives rise to exact iterative solutions to the finite and infinite horizon stochastic optimal control problem, while direct application of Bayesian inference methods yields instances of risk sensitive control. 1
LogDeterminant Relaxation for Approximate Inference in Discrete Markov Random Fields
, 2006
"... Graphical models are well suited to capture the complex and nonGaussian statistical dependencies that arise in many realworld signals. A fundamental problem common to any signal processing application of a graphical model is that of computing approximate marginal probabilities over subsets of nod ..."
Abstract

Cited by 41 (3 self)
 Add to MetaCart
logdeterminant maximization problem that can be solved efficiently by interior point methods, thereby providing approximations to the exact marginals. We show how a slightly weakened logdeterminant relaxation can be solved even more efficiently by a dual reformulation. When applied to denoising
Approximate inference using conditional entropy decompositions
"... We introduce a novel method for estimating the partition function and marginals of distributions defined using graphical models. The method uses the entropy chain rule to obtain an upper bound on the entropy of a distribution given marginal distributions of variable subsets. The structure of the b ..."
Abstract

Cited by 17 (2 self)
 Add to MetaCart
of the bound is determined by a permutation, or elimination order, of the model variables. Optimizing this bound results in an upper bound on the log partition function, and also yields an approximation to the model marginals. The optimization problem is convex, and is in fact a dual of a geometric program. We
Dual Decomposition Inference for Graphical Models over Strings
, 2015
"... We investigate dual decomposition for joint MAP inference of many strings. Given an arbitrary graphical model, we decompose it into small acyclic submodels, whose MAP configurations can be found by finitestate composition and dynamic programming. We force the solutions of these subproblems to agre ..."
Abstract
 Add to MetaCart
We investigate dual decomposition for joint MAP inference of many strings. Given an arbitrary graphical model, we decompose it into small acyclic submodels, whose MAP configurations can be found by finitestate composition and dynamic programming. We force the solutions of these subproblems
Results 1  10
of
114