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» Machine learning problems from optimization perspective
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NIPS
2008
15 years 4 months ago
Analyzing human feature learning as nonparametric Bayesian inference
Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw o...
Joseph Austerweil, Thomas L. Griffiths
148
Voted
ALGORITHMICA
2002
120views more  ALGORITHMICA 2002»
15 years 3 months ago
An Experimental Study of Algorithms for Weighted Completion Time Scheduling
We consider the total weighted completion time scheduling problem for parallel identical machines and precedence constraints, P jprecj PwiCi. This important and broad class of pro...
Ivan D. Baev, Waleed Meleis, Alexandre E. Eichenbe...
ICCV
2005
IEEE
15 years 9 months ago
Priors for People Tracking from Small Training Sets
We advocate the use of Scaled Gaussian Process Latent Variable Models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a...
Raquel Urtasun, David J. Fleet, Aaron Hertzmann, P...
127
Voted
ICML
2000
IEEE
16 years 4 months ago
Rates of Convergence for Variable Resolution Schemes in Optimal Control
This paper presents a general method to derive tight rates of convergence for numerical approximations in optimal control when we consider variable resolution grids. We study the ...
Andrew W. Moore, Rémi Munos
AAMAS
2007
Springer
15 years 3 months ago
Parallel Reinforcement Learning with Linear Function Approximation
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...
Matthew Grounds, Daniel Kudenko