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CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 1 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
MOBIHOC
2008
ACM
14 years 5 months ago
An approximation algorithm for conflict-aware broadcast scheduling in wireless ad hoc networks
Broadcast scheduling is a fundamental problem in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in ...
Reza Mahjourian, Feng Chen, Ravi Tiwari, My T. Tha...
IJCAI
1989
13 years 7 months ago
Coping With Uncertainty in Map Learning
In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refe...
Kenneth Basye, Thomas Dean, Jeffrey Scott Vitter
STOC
1994
ACM
128views Algorithms» more  STOC 1994»
13 years 9 months ago
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
We present new results on the well-studied problem of learning DNF expressions. We prove that an algorithm due to Kushilevitz and Mansour [13] can be used to weakly learn DNF form...
Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, ...
LICS
2002
IEEE
13 years 10 months ago
Probabilistic Abstraction for Model Checking: An Approach Based on Property Testing
istic Abstraction for Model Checking: an Approach Based on Property Testing∗ Sophie Laplante† Richard Lassaigne‡ Fr´ed´eric Magniez§ Sylvain Peyronnet† Michel de Rougemo...
Sophie Laplante, Richard Lassaigne, Fréd&ea...