Ultrafast Monte Carlo for kernel estimators and generalized statistical summations

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

  Holmes, Michael P ^a   Gray, Alexander G ^a   Isbell, Charles Lee ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ERRORS; FORESTRY; LEARNING SYSTEMS; STOCHASTIC SYSTEMS;

COMPUTATIONAL BOTTLENECKS; ERROR CONTROL; KERNEL ESTIMATORS; LARGE DATASETS; MACHINE-LEARNING; MONTECARLO METHODS; MULTI-STAGES; PROBABILISTICS; RELATIVE ERRORS; ULTRA-FAST;

MONTE CARLO METHODS;

EID: 85162033847     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Nicol N. Schraudolph and Thore Graepel. Combining conjugate direction methods with stochastic approximation of gradients. In Workshop on Artificial Intelligence and Statistics (AISTATS), 2003.
2. Alexander G. Gray and AndrewW. Moore. N-body problems in statistical learning. In Advances in Neural Information Processing Systems (NIPS) 13, 2000.
3. Mike Klaas, Mark Briers, Nando de Freitas, and Arnaud Doucet. Fast particle smoothing: If I had a million particles. In International Conference on Machine Learning (ICML), 2006.
4. PingWang, Dongryeol Lee, Alexander Gray, and James M. Rehg. Fast mean shift with accurate and stable convergence. In Workshop on Artificial Intelligence and Statistics (AISTATS), 2007.
5. Michael P. Holmes, Alexander G. Gray, and Charles Lee Isbell Jr. Fast nonparametric conditional density estimation. In Uncertainty in Artificial Intelligence (UAI), 2007.
6. Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. John Wiley & Sons, 1981.
7. Paul Glasserman. Monte Carlo methods in financial engineering. Springer-Verlag, 2004.