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CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 3 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 7 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 8 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 11 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
14 years 12 days 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...