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» Efficiently solving convex relaxations for MAP estimation
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CORR
2007
Springer
128views Education» more  CORR 2007»
14 years 9 months ago
Model Selection Through Sparse Maximum Likelihood Estimation
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
ICML
2010
IEEE
14 years 10 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
CIMAGING
2010
195views Hardware» more  CIMAGING 2010»
14 years 11 months ago
SPIRAL out of convexity: sparsity-regularized algorithms for photon-limited imaging
The observations in many applications consist of counts of discrete events, such as photons hitting a detector, which cannot be effectively modeled using an additive bounded or Ga...
Zachary T. Harmany, Roummel F. Marcia, Rebecca Wil...
WSC
2008
14 years 12 months ago
Optimizing portfolio tail measures: Asymptotics and efficient simulation optimization
We consider a portfolio allocation problem where the objective function is a tail event such as probability of large portfolio losses. The dependence between assets is captured th...
Sandeep Juneja
JCNS
2010
103views more  JCNS 2010»
14 years 4 months ago
Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-spa
A number of important data analysis problems in neuroscience can be solved using state-space models. In this article, we describe fast methods for computing the exact maximum a pos...
Shinsuke Koyama, Liam Paninski