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Partially exchangeable networks and architectures for learning summary statistics in approximate Bayesian computation

Wiqvist, Samuel LU ; Mattei, Pierre Alexandre ; Picchini, Umberto LU and Frellsen, Jes (2019) 36th International Conference on Machine Learning, ICML 2019 In 36th International Conference on Machine Learning, ICML 2019 2019-June. p.11795-11804
Abstract

We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show that any block-switch invariant function has a PEN-like representation. The DeepSets architecture is a special case of PEN and we can therefore also target fully exchangeable data. We employ PENs to learn summary statistics in approximate Bayesian computation (ABC). When comparing PENs to previous deep learning methods for learning summary statistics, our results are highly competitive, both considering time series and... (More)

We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show that any block-switch invariant function has a PEN-like representation. The DeepSets architecture is a special case of PEN and we can therefore also target fully exchangeable data. We employ PENs to learn summary statistics in approximate Bayesian computation (ABC). When comparing PENs to previous deep learning methods for learning summary statistics, our results are highly competitive, both considering time series and static models. Indeed, PENs provide more reliable posterior samples even when using less training data.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
36th International Conference on Machine Learning, ICML 2019
series title
36th International Conference on Machine Learning, ICML 2019
volume
2019-June
pages
10 pages
publisher
International Machine Learning Society (IMLS)
conference name
36th International Conference on Machine Learning, ICML 2019
conference location
Long Beach, United States
conference dates
2019-06-09 - 2019-06-15
external identifiers
  • scopus:85078252544
ISBN
9781510886988
language
English
LU publication?
yes
id
3c47b80e-9d83-4333-af07-d317d6bae77d
date added to LUP
2020-02-07 13:08:59
date last changed
2022-04-18 20:21:04
@inproceedings{3c47b80e-9d83-4333-af07-d317d6bae77d,
  abstract     = {{<p>We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show that any block-switch invariant function has a PEN-like representation. The DeepSets architecture is a special case of PEN and we can therefore also target fully exchangeable data. We employ PENs to learn summary statistics in approximate Bayesian computation (ABC). When comparing PENs to previous deep learning methods for learning summary statistics, our results are highly competitive, both considering time series and static models. Indeed, PENs provide more reliable posterior samples even when using less training data.</p>}},
  author       = {{Wiqvist, Samuel and Mattei, Pierre Alexandre and Picchini, Umberto and Frellsen, Jes}},
  booktitle    = {{36th International Conference on Machine Learning, ICML 2019}},
  isbn         = {{9781510886988}},
  language     = {{eng}},
  pages        = {{11795--11804}},
  publisher    = {{International Machine Learning Society (IMLS)}},
  series       = {{36th International Conference on Machine Learning, ICML 2019}},
  title        = {{Partially exchangeable networks and architectures for learning summary statistics in approximate Bayesian computation}},
  volume       = {{2019-June}},
  year         = {{2019}},
}