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» Approximate Expectation Maximization
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133
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NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 7 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
128
Voted
ICDCS
2007
IEEE
15 years 7 months ago
Optimizing Multicast Performance in Large-Scale WLANs
Support for efficient multicasting in WLANs can enable new services such as streaming TV channels, radio channels, and visitor's information. With increasing deployments of l...
Ai Chen, Dongwook Lee, Prasun Sinha
147
Voted
ICALP
2010
Springer
15 years 5 months ago
On the Limitations of Greedy Mechanism Design for Truthful Combinatorial Auctions
We study the combinatorial auction (CA) problem, in which m objects are sold to rational agents and the goal is to maximize social welfare. Of particular interest is the special ca...
Allan Borodin, Brendan Lucier
110
Voted
WSC
2004
15 years 5 months ago
A Large Deviations Perspective on Ordinal Optimization
We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studi...
Peter W. Glynn, Sandeep Juneja
97
Voted
NIPS
2003
15 years 5 months ago
Convex Methods for Transduction
The 2-class transduction problem, as formulated by Vapnik [1], involves finding a separating hyperplane for a labelled data set that is also maximally distant from a given set of...
Tijl De Bie, Nello Cristianini