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JMLR
2012
9 years 7 months ago
Adaptive MCMC with Bayesian Optimization
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to nondifferentiable objective functions and trades off explor...
Nimalan Mahendran, Ziyu Wang, Firas Hamze, Nando d...
CORR
2012
Springer
170views Education» more  CORR 2012»
10 years 28 days ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
CORR
2012
Springer
198views Education» more  CORR 2012»
10 years 28 days ago
Lipschitz Parametrization of Probabilistic Graphical Models
We show that the log-likelihood of several probabilistic graphical models is Lipschitz continuous with respect to the ￿p-norm of the parameters. We discuss several implications ...
Jean Honorio
AAAI
2011
10 years 5 months ago
Heterogeneous Transfer Learning with RBMs
A common approach in machine learning is to use a large amount of labeled data to train a model. Usually this model can then only be used to classify data in the same feature spac...
Bin Wei, Christopher Pal
BMCBI
2011
10 years 8 months ago
A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-w
Background: Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1...
Raphael Mourad, Christine Sinoquet, Philippe Leray
JMLR
2010
169views more  JMLR 2010»
11 years 11 hour ago
Focused Belief Propagation for Query-Specific Inference
With the increasing popularity of largescale probabilistic graphical models, even "lightweight" approximate inference methods are becoming infeasible. Fortunately, often...
Anton Chechetka, Carlos Guestrin
ICMLA
2010
11 years 3 months ago
A Probabilistic Graphical Model of Quantum Systems
Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomp...
Chen-Hsiang Yeang
ML
2006
ACM
131views Machine Learning» more  ML 2006»
11 years 5 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos
FTML
2008
185views more  FTML 2008»
11 years 5 months ago
Graphical Models, Exponential Families, and Variational Inference
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
Martin J. Wainwright, Michael I. Jordan
NIPS
2003
11 years 6 months ago
Approximability of Probability Distributions
We consider the question of how well a given distribution can be approximated with probabilistic graphical models. We introduce a new parameter, effective treewidth, that captures...
Alina Beygelzimer, Irina Rish
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