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» On Approximating Multidimensional Probability Distributions ...
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TROB
2010
129views more  TROB 2010»
13 years 7 months ago
A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control
—Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation,...
Lars Blackmore, Masahiro Ono, Askar Bektassov, Bri...
IJAR
2008
99views more  IJAR 2008»
13 years 8 months ago
Unifying practical uncertainty representations. II: Clouds
There exist many tools for capturing imprecision in probabilistic representations. Among them are random sets, possibility distributions, probability intervals, and the more recen...
Sébastien Destercke, Didier Dubois, Eric Ch...
CVPR
2011
IEEE
13 years 4 months ago
Learning Message-Passing Inference Machines for Structured Prediction
Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models prov...
Stephane Ross, Daniel Munoz, J. Andrew Bagnell
CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 4 months 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
2008
Springer
88views Education» more  CORR 2008»
13 years 8 months ago
Lower bounds for distributed markov chain problems
We study the worst-case communication complexity of distributed algorithms computing a path problem based on stationary distributions of random walks in a network G with the caveat...
Rahul Sami, Andy Twigg