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» Approximating Gaussian Processes with H2-Matrices
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ECML
2007
Springer
9 years 7 months ago
Approximating Gaussian Processes with H2-Matrices
Steffen Börm, Jochen Garcke
IROS
2007
IEEE
168views Robotics» more  IROS 2007»
9 years 12 months ago
Improving humanoid locomotive performance with learnt approximated dynamics via Gaussian processes for regression
Abstract— We propose to improve the locomotive performance of humanoid robots by using approximated biped stepping and walking dynamics with reinforcement learning (RL). Although...
Jun Morimoto, Christopher G. Atkeson, Gen Endo, Go...
AAAI
2015
4 years 2 months ago
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the data size. To improve its scalability, this paper presents a low-rank-cum-Markov a...
Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jai...
ICML
2009
IEEE
10 years 6 months ago
Analytic moment-based Gaussian process filtering
We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matr...
Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hane...
ICML
2008
IEEE
10 years 6 months ago
Gaussian process product models for nonparametric nonstationarity
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
Ryan Prescott Adams, Oliver Stegle
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