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SAGA
2009
Springer
14 years 3 months ago
Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible dec...
Boris Defourny, Damien Ernst, Louis Wehenkel
KDD
2007
ACM
197views Data Mining» more  KDD 2007»
14 years 9 months ago
Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming
The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kerne...
Jieping Ye, Shuiwang Ji, Jianhui Chen
KDD
2010
ACM
245views Data Mining» more  KDD 2010»
13 years 10 months ago
Learning incoherent sparse and low-rank patterns from multiple tasks
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the spa...
Jianhui Chen, Ji Liu, Jieping Ye
TIT
1998
112views more  TIT 1998»
13 years 8 months ago
Application of Network Calculus to Guaranteed Service Networks
—We use recent network calculus results to study some properties of lossless multiplexing as it may be used in guaranteed service networks. We call network calculus a set of resu...
Jean-Yves Le Boudec
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