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CVPR
2012
IEEE
11 years 11 months ago
What is optimized in tight convex relaxations for multi-label problems?
In this work we present a unified view on Markov random fields and recently proposed continuous tight convex relaxations for multi-label assignment in the image plane. These rel...
Christopher Zach, Christian Hane, Marc Pollefeys
NIPS
2007
13 years 10 months ago
Convex Learning with Invariances
Incorporating invariances into a learning algorithm is a common problem in machine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbit...
Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex...
CORR
2011
Springer
177views Education» more  CORR 2011»
13 years 3 months ago
A Truthful Randomized Mechanism for Combinatorial Public Projects via Convex Optimization
In Combinatorial Public Projects, there is a set of projects that may be undertaken, and a set of selfinterested players with a stake in the set of projects chosen. A public plann...
Shaddin Dughmi
IOR
2007
119views more  IOR 2007»
13 years 8 months ago
A Decentralized Approach to Discrete Optimization via Simulation: Application to Network Flow
We study a new class of decentralized algorithms for discrete optimization via simulation, which is inspired by the fictitious play algorithm applied to games with identical inte...
Alfredo Garcia, Stephen D. Patek, Kaushik Sinha
SAGT
2010
Springer
200views Game Theory» more  SAGT 2010»
13 years 7 months ago
2-Player Nash and Nonsymmetric Bargaining Games: Algorithms and Structural Properties
The solution to a Nash or a nonsymmetric bargaining game is obtained by maximizing a concave function over a convex set, i.e., it is the solution to a convex program. We show that...
Vijay V. Vazirani