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» Learning Submodular Functions
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FOCS
2008
IEEE
14 years 5 months ago
Submodular Approximation: Sampling-based Algorithms and Lower Bounds
We introduce several generalizations of classical computer science problems obtained by replacing simpler objective functions with general submodular functions. The new problems i...
Zoya Svitkina, Lisa Fleischer
CORR
2010
Springer
144views Education» more  CORR 2010»
13 years 11 months ago
Efficient Minimization of Decomposable Submodular Functions
Many combinatorial problems arising in machine learning can be reduced to the problem of minimizing a submodular function. Submodular functions are a natural discrete analog of co...
Peter Stobbe, Andreas Krause
JMLR
2010
187views more  JMLR 2010»
13 years 5 months ago
SFO: A Toolbox for Submodular Function Optimization
In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns proper...
Andreas Krause
CORR
2010
Springer
146views Education» more  CORR 2010»
13 years 11 months ago
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously diffic...
Daniel Golovin, Andreas Krause
CORR
2012
Springer
228views Education» more  CORR 2012»
12 years 6 months ago
Active Semi-Supervised Learning using Submodular Functions
Andrew Guillory, Jeff A. Bilmes