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DiCE: The infinitely differentiable Monte Carlo estimator

Abstract:

The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), e.g., in reinforcement learning and meta-learning. While deriving the first order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher order derivatives is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not complia...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
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Engineering and Physical Sciences Research Council More from this funder
Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
35th International Conference on Machine Learning (ICML 2018) Journal website
Host title:
35th International Conference on Machine Learning (ICML 2018)
Publication date:
2018-07-03
Acceptance date:
2018-06-12
Source identifiers:
857026
Pubs id:
pubs:857026
UUID:
uuid:4cc58c06-d591-498a-9d67-05f359356931
Local pid:
pubs:857026
Deposit date:
2018-06-12

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