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» On the Convergence of Bound Optimization Algorithms
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NIPS
2008
13 years 9 months ago
On the Generalization Ability of Online Strongly Convex Programming Algorithms
This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound...
Sham M. Kakade, Ambuj Tewari
TIT
2008
127views more  TIT 2008»
13 years 7 months ago
Max-Product for Maximum Weight Matching: Convergence, Correctness, and LP Duality
Abstract--Max-product "belief propagation" (BP) is an iterative, message-passing algorithm for finding the maximum a posteriori (MAP) assignment of a discrete probability...
Mohsen Bayati, Devavrat Shah, Mayank Sharma
JOTA
2010
117views more  JOTA 2010»
13 years 5 months ago
Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
We consider a distributed multi-agent network system where the goal is to minimize a sum of agent objective functions subject to a common set of constraints. For this problem, we p...
S. Sundhar Ram, Angelia Nedic, Venugopal V. Veerav...
NIPS
2008
13 years 9 months ago
Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm
We study the convergence and the rate of convergence of a local manifold learning algorithm: LTSA [13]. The main technical tool is the perturbation analysis on the linear invarian...
Andrew Smith, Xiaoming Huo, Hongyuan Zha
TIT
2002
86views more  TIT 2002»
13 years 7 months ago
Lagrangian empirical design of variable-rate vector quantizers: consistency and convergence rates
Abstract--The Lagrangian formulation of variable-rate vector quantization is known to yield useful necessary conditions for quantizer optimality and generalized Lloyd algorithms fo...
Tamás Linder