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IBPRIA
2007
Springer
13 years 11 months ago
A Density-Based Data Reduction Algorithm for Robust Estimators
In this paper we present a non parametric density-based data reduction technique designed to be used in robust parameter estimation problems. Existing approaches are focused on red...
Luis Ferraz, Ramon Lluis Felip, Brais Martí...
COLT
2000
Springer
14 years 1 months ago
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning
We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process (  ¢¡¤£¦¥§  ), and focus on gradient ascent approache...
Peter L. Bartlett, Jonathan Baxter
ICASSP
2011
IEEE
13 years 1 months ago
Sub-Nyquist sampling of short pulses
—We develop sub-Nyquist sampling systems for analog signals comprised of several, possibly overlapping, finite duration pulses with unknown shapes and time positions. Efficient...
Ewa Matusiak, Yonina C. Eldar
GECCO
2008
Springer
171views Optimization» more  GECCO 2008»
13 years 10 months ago
An EDA based on local markov property and gibbs sampling
The key ideas behind most of the recently proposed Markov networks based EDAs were to factorise the joint probability distribution in terms of the cliques in the undirected graph....
Siddhartha Shakya, Roberto Santana
CVPR
2010
IEEE
12 years 6 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu