Conditionally Gaussian PAC-Bayes
Recent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradient descent. Most of these procedures need to replace the misclassification error with a surrogate loss, leading to a mis...
| Asıl Yazarlar: | , , |
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| Materyal Türü: | Conference item |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
Journal of Machine Learning Research
2022
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| _version_ | 1826309127496794112 |
|---|---|
| author | Clerico, E Deligiannidis, G Doucet, A |
| author_facet | Clerico, E Deligiannidis, G Doucet, A |
| author_sort | Clerico, E |
| collection | OXFORD |
| description | Recent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradient descent. Most of these procedures need to replace the misclassification error with a surrogate loss, leading to a mismatch between the optimisation objective and the actual generalisation bound. The present paper proposes a novel training algorithm that optimises the PAC-Bayesian bound, without relying on any surrogate loss. Empirical results show that this approach outperforms currently available PAC-Bayesian training methods. |
| first_indexed | 2024-03-07T07:29:34Z |
| format | Conference item |
| id | oxford-uuid:e08aa158-f6ff-4812-980b-c9e3673606cf |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:29:34Z |
| publishDate | 2022 |
| publisher | Journal of Machine Learning Research |
| record_format | dspace |
| spelling | oxford-uuid:e08aa158-f6ff-4812-980b-c9e3673606cf2022-12-13T17:26:41ZConditionally Gaussian PAC-BayesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:e08aa158-f6ff-4812-980b-c9e3673606cfEnglishSymplectic ElementsJournal of Machine Learning Research2022Clerico, EDeligiannidis, GDoucet, ARecent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradient descent. Most of these procedures need to replace the misclassification error with a surrogate loss, leading to a mismatch between the optimisation objective and the actual generalisation bound. The present paper proposes a novel training algorithm that optimises the PAC-Bayesian bound, without relying on any surrogate loss. Empirical results show that this approach outperforms currently available PAC-Bayesian training methods. |
| spellingShingle | Clerico, E Deligiannidis, G Doucet, A Conditionally Gaussian PAC-Bayes |
| title | Conditionally Gaussian PAC-Bayes |
| title_full | Conditionally Gaussian PAC-Bayes |
| title_fullStr | Conditionally Gaussian PAC-Bayes |
| title_full_unstemmed | Conditionally Gaussian PAC-Bayes |
| title_short | Conditionally Gaussian PAC-Bayes |
| title_sort | conditionally gaussian pac bayes |
| work_keys_str_mv | AT clericoe conditionallygaussianpacbayes AT deligiannidisg conditionallygaussianpacbayes AT douceta conditionallygaussianpacbayes |