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JMLR
2012
8 years 6 months ago
Generic Methods for Optimization-Based Modeling
“Energy” models for continuous domains can be applied to many problems, but often suffer from high computational expense in training, due to the need to repeatedly minimize t...
Justin Domke
SIGMOD
2012
ACM
232views Database» more  SIGMOD 2012»
8 years 6 months ago
Large-scale machine learning at twitter
The success of data-driven solutions to difficult problems, along with the dropping costs of storing and processing massive amounts of data, has led to growing interest in largesc...
Jimmy Lin, Alek Kolcz
EMMCVPR
2011
Springer
9 years 3 months ago
Discrete Optimization of the Multiphase Piecewise Constant Mumford-Shah Functional
Abstract. The Mumford-Shah model has been one of the most powerful models in image segmentation and denoising. The optimization of the multiphase Mumford-Shah energy functional has...
Noha Youssry El-Zehiry, Leo Grady
CIKM
2011
Springer
9 years 4 months ago
Towards feature selection in network
Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed in a net...
Quanquan Gu, Jiawei Han
CEC
2011
IEEE
9 years 4 months ago
Stochastic Natural Gradient Descent by estimation of empirical covariances
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
Luigi Malagò, Matteo Matteucci, Giovanni Pi...
IJCV
2011
163views more  IJCV 2011»
9 years 7 months ago
Operator Splittings, Bregman Methods and Frame Shrinkage in Image Processing
We examine the underlying structure of popular algorithms for variational methods used in image processing. We focus here on operator splittings and Bregman methods based on a uni...
Simon Setzer
CDC
2010
IEEE
138views Control Systems» more  CDC 2010»
9 years 11 months ago
Sensor-based robot deployment algorithms
Abstract-- In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For...
Jerome Le Ny, George J. Pappas
TSP
2008
106views more  TSP 2008»
10 years 4 months ago
Guaranteeing Practical Convergence in Algorithms for Sensor and Source Localization
This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise perfor...
Baris Fidan, Soura Dasgupta, Brian D. O. Anderson
IJON
2007
134views more  IJON 2007»
10 years 4 months ago
Analysis of SVM regression bounds for variable ranking
This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and...
Alain Rakotomamonjy
CORR
2010
Springer
103views Education» more  CORR 2010»
10 years 4 months ago
On the Finite Time Convergence of Cyclic Coordinate Descent Methods
Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stab...
Ankan Saha, Ambuj Tewari
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