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» Regret Bounds for Prediction Problems
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NIPS
2007
13 years 11 months ago
Structured Learning with Approximate Inference
In many structured prediction problems, the highest-scoring labeling is hard to compute exactly, leading to the use of approximate inference methods. However, when inference is us...
Alex Kulesza, Fernando Pereira
CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
13 years 10 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
MOR
2007
149views more  MOR 2007»
13 years 9 months ago
LP Rounding Approximation Algorithms for Stochastic Network Design
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
Anupam Gupta, R. Ravi, Amitabh Sinha
WAW
2010
Springer
270views Algorithms» more  WAW 2010»
13 years 7 months ago
Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large Networks
Abstract. Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topologica...
Pooya Esfandiar, Francesco Bonchi, David F. Gleich...
JCP
2007
108views more  JCP 2007»
13 years 9 months ago
A Genetic Algorithm Method to Assimilate Sensor Data for a Toxic Contaminant Release
— Following a toxic contaminant release, either accidental or intentional, predicting the transport and dispersion of the contaminant becomes a critical problem for Homeland Defe...
Sue Ellen Haupt, George S. Young, Christopher T. A...