297 search results - page 2 / 60 » Adaptive Importance Sampling for Estimation in Structured Do... |
WSC
2007
13 years 8 months ago
2007
Importance Sampling is a potentially powerful variance reduction technique to speed up simulations where the objective depends on the occurrence of rare events. However, it is cru...
AAAI
2008
13 years 8 months ago
2008
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
ICML
2008
IEEE
14 years 6 months ago
2008
IEEE
Typically agent evaluation is done through Monte Carlo estimation. However, stochastic agent decisions and stochastic outcomes can make this approach inefficient, requiring many s...
NIPS
2007
13 years 7 months ago
2007
A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likeli...
ECCV
2002
Springer
14 years 7 months ago
2002
Springer
Sequential random sampling (`Markov Chain Monte-Carlo') is a popular strategy for many vision problems involving multimodal distributions over high-dimensional parameter spac...