Between integrals and optima: new methods for scalable machine learning
<p>The success of machine learning is due in part to the effectiveness of scalable computational methods, like stochastic gradient descent or Monte Carlo methods, that undergird learning algorithms. This thesis contributes four new scalable methods for distinct problems that arise in machine l...
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| Format: | Thesis |
| Language: | English |
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2020
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| _version_ | 1817932205454786560 |
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| author | Maddison, C |
| author2 | Doucet, A |
| author_facet | Doucet, A Maddison, C |
| author_sort | Maddison, C |
| collection | OXFORD |
| description | <p>The success of machine learning is due in part to the effectiveness of scalable computational methods, like stochastic gradient descent or Monte Carlo methods, that undergird learning algorithms. This thesis contributes four new scalable methods for distinct problems that arise in machine learning. It introduces a new method for gradient estimation in discrete variable models, a new objective for maximum likelihood learning in the presence of latent variables, and two new gradient-based differentiable optimization methods. Although quite different, these contributions address distinct, critical parts of a typical machine learning workflow. Furthermore, each contribution is inspired by an interplay between the numerical problems of optimization and integration, an interplay that forms the central theme of this thesis.</p> |
| first_indexed | 2024-03-06T19:40:51Z |
| format | Thesis |
| id | oxford-uuid:20a0c676-c9a6-4837-9164-4858a28ee9b8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:34:13Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | oxford-uuid:20a0c676-c9a6-4837-9164-4858a28ee9b82024-12-01T17:46:24ZBetween integrals and optima: new methods for scalable machine learningThesishttp://purl.org/coar/resource_type/c_db06uuid:20a0c676-c9a6-4837-9164-4858a28ee9b8Machine LearningEnglishHyrax Deposit2020Maddison, CDoucet, ATeh, YW<p>The success of machine learning is due in part to the effectiveness of scalable computational methods, like stochastic gradient descent or Monte Carlo methods, that undergird learning algorithms. This thesis contributes four new scalable methods for distinct problems that arise in machine learning. It introduces a new method for gradient estimation in discrete variable models, a new objective for maximum likelihood learning in the presence of latent variables, and two new gradient-based differentiable optimization methods. Although quite different, these contributions address distinct, critical parts of a typical machine learning workflow. Furthermore, each contribution is inspired by an interplay between the numerical problems of optimization and integration, an interplay that forms the central theme of this thesis.</p> |
| spellingShingle | Machine Learning Maddison, C Between integrals and optima: new methods for scalable machine learning |
| title | Between integrals and optima: new methods for scalable machine learning |
| title_full | Between integrals and optima: new methods for scalable machine learning |
| title_fullStr | Between integrals and optima: new methods for scalable machine learning |
| title_full_unstemmed | Between integrals and optima: new methods for scalable machine learning |
| title_short | Between integrals and optima: new methods for scalable machine learning |
| title_sort | between integrals and optima new methods for scalable machine learning |
| topic | Machine Learning |
| work_keys_str_mv | AT maddisonc betweenintegralsandoptimanewmethodsforscalablemachinelearning |