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ICML
2009
IEEE
16 years 8 months ago
The Bayesian group-Lasso for analyzing contingency tables
Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Ba...
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edga...
ICML
2008
IEEE
16 years 8 months ago
Fully distributed EM for very large datasets
In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of th...
Jason Wolfe, Aria Haghighi, Dan Klein
ICML
2008
IEEE
16 years 8 months ago
An object-oriented representation for efficient reinforcement learning
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...
Carlos Diuk, Andre Cohen, Michael L. Littman
ICML
2008
IEEE
16 years 8 months ago
Non-parametric policy gradients: a unified treatment of propositional and relational domains
Policy gradient approaches are a powerful instrument for learning how to interact with the environment. Existing approaches have focused on propositional and continuous domains on...
Kristian Kersting, Kurt Driessens
ICML
1996
IEEE
16 years 8 months ago
Discretizing Continuous Attributes While Learning Bayesian Networks
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
Moisés Goldszmidt, Nir Friedman
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