Importance sampling: a review
By Surya T. Tokdar, Robert E. Kass. 2010
478 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/929f3b0b1e63d96ed2c3942ae4eaa88d6bfbf8bf
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Sergey Levine
- Aaron C. Courville
- Kyunghyun Cho
- Pieter Abbeel
- Quoc V. Le
- Percy Liang
- Mohit Bansal
In Management Science and Operations Research
- Jeffrey Mahler
- Ken Goldberg
- Yoshua Bengio
- Aaron C. Courville
- Guillaume Alain
- Anca Dragan
- Michael Laskey
- Alex Lamb
In Statistics and Probability
- Yoshua Bengio
- Aaron C. Courville
- Guillaume Alain
- Ken Goldberg
- Chinnadhurai Sankar
- Anca Dragan
- Michael Laskey
- Tijmen Blankevoort
Citations in the corpus
- SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces (2016, background): “Risk via Novelty Detection The main motivation for our risk definition is the notion that a trained model will be able to generalize within the distribution it is trained on [31], making states outside of this distribution risky.”
- Variance Reduction in SGD by Distributed Importance Sampling (2015, methodology): “The method that we present in this paper will incorporate that intuition into a training algorithm that is justified by theory rooted in importance sampling (Tokdar & Kass, 2010).”