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    UAI
    2008
    252views Artificial Intelligence» more  UAI 2008»
    Small Sample Inference for Generalization Error in Classification Using the CUD Bound
    13 years 6 months ago
    Small Sample Inference for Generalization Error in Classification Using the CUD Bound
    Download  www.stat.lsa.umich.edu
    Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization err...
    Eric Laber, Susan Murphy
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    UAI
    2008
    240views Artificial Intelligence» more  UAI 2008»
    AND/OR Importance Sampling
    13 years 6 months ago
    AND/OR Importance Sampling
    Download  www.ics.uci.edu
    The paper introduces an AND/OR importance sampling scheme for probabilistic graphical models. In contrast to conventional importance sampling, AND/OR importance sampling caches sa...
    Vibhav Gogate, Rina Dechter
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    UAI
    2008
    214views Artificial Intelligence» more  UAI 2008»
    Adaptive inference on general graphical models
    13 years 6 months ago
    Adaptive inference on general graphical models
    Download  people.cs.uchicago.edu
    Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform upd...
    Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mett...
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    UAI
    2008
    204views Artificial Intelligence» more  UAI 2008»
    Bayesian network learning by compiling to weighted MAX-SAT
    13 years 6 months ago
    Bayesian network learning by compiling to weighted MAX-SAT
    Download  www.maths.dur.ac.uk
    James Cussens
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    UAI
    2008
    185views Artificial Intelligence» more  UAI 2008»
    Learning Arithmetic Circuits
    13 years 6 months ago
    Learning Arithmetic Circuits
    Download  www.cs.washington.edu
    Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
    Daniel Lowd, Pedro Domingos
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    UAI
    2008
    290views Artificial Intelligence» more  UAI 2008»
    Continuous Time Dynamic Topic Models
    13 years 6 months ago
    Continuous Time Dynamic Topic Models
    Download  www.cs.princeton.edu
    In this paper, we develop the continuous time dynamic topic model (cDTM). The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequenti...
    Chong Wang, David M. Blei, David Heckerman
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    UAI
    2008
    192views Artificial Intelligence» more  UAI 2008»
    Sparse Stochastic Finite-State Controllers for POMDPs
    13 years 6 months ago
    Sparse Stochastic Finite-State Controllers for POMDPs
    Download  www.aaai.org
    Bounded policy iteration is an approach to solving infinitehorizon POMDPs that represents policies as stochastic finitestate controllers and iteratively improves a controller by a...
    Eric A. Hansen
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    UAI
    2008
    333views Artificial Intelligence» more  UAI 2008»
    Hybrid Variational/Gibbs Collapsed Inference in Topic Models
    13 years 6 months ago
    Hybrid Variational/Gibbs Collapsed Inference in Topic Models
    Download  uai2008.cs.helsinki.fi
    Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...
    Max Welling, Yee Whye Teh, Bert Kappen
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    UAI
    2008
    223views Artificial Intelligence» more  UAI 2008»
    The Computational Complexity of Sensitivity Analysis and Parameter Tuning
    13 years 6 months ago
    The Computational Complexity of Sensitivity Analysis and Parameter Tuning
    Download  uai2008.cs.helsinki.fi
    While known algorithms for sensitivity analysis and parameter tuning in probabilistic networks have a running time that is exponential in the size of the network, the exact comput...
    Johan Kwisthout, Linda C. van der Gaag
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    UAI
    2008
    239views Artificial Intelligence» more  UAI 2008»
    Constrained Approximate Maximum Entropy Learning of Markov Random Fields
    13 years 6 months ago
    Constrained Approximate Maximum Entropy Learning of Markov Random Fields
    Download  uai2008.cs.helsinki.fi
    Varun Ganapathi, David Vickrey, John Duchi, Daphne...
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