Add to ORKGIn this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the corresponding partition function. We show that our method excels in the typical "high signal - high coupling" regime that results in ragged energy landscapes difficult for alternative approaches. Copyright 2012 by the author(s)/owner(s)
We give an algorithm that, with high probability, recovers a planted k-partition in a random graph, ...
Perturbation models are families of distri-butions induced from perturbations. They combine randomiz...
Perturbation models are families of distri-butions induced from perturbations. They combine randomiz...
In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributio...
In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributio...
The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference...
The maximum a-posteriori (MAP) pertur-bation framework has emerged as a useful approach for inferenc...
The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference...
This electronic version was submitted by the student author. The certified thesis is available in th...
One approach to modeling structured dis-crete data is to describe the probability of states via an e...
Markov random field (MRF) model provides an elegant probabilistic framework to formulate inter-depen...
One approach to modeling structured discrete data is to describe the probability of states via an en...
In this work we develop efficient methods for learning random MAP predictors for structured label pr...
Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized ...
The Markov Random Field (MRF) MAP inference problem is considered from the viewpoint ofinteger progr...
We give an algorithm that, with high probability, recovers a planted k-partition in a random graph, ...
Perturbation models are families of distri-butions induced from perturbations. They combine randomiz...
Perturbation models are families of distri-butions induced from perturbations. They combine randomiz...
In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributio...
In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributio...
The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference...
The maximum a-posteriori (MAP) pertur-bation framework has emerged as a useful approach for inferenc...
The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference...
This electronic version was submitted by the student author. The certified thesis is available in th...
One approach to modeling structured dis-crete data is to describe the probability of states via an e...
Markov random field (MRF) model provides an elegant probabilistic framework to formulate inter-depen...
One approach to modeling structured discrete data is to describe the probability of states via an en...
In this work we develop efficient methods for learning random MAP predictors for structured label pr...
Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized ...
The Markov Random Field (MRF) MAP inference problem is considered from the viewpoint ofinteger progr...
We give an algorithm that, with high probability, recovers a planted k-partition in a random graph, ...
Perturbation models are families of distri-butions induced from perturbations. They combine randomiz...
Perturbation models are families of distri-butions induced from perturbations. They combine randomiz...