Probabilistic programming with programmable inference
© 2018 Copyright held by the owner/author(s). We introduce inference metaprogramming for probabilistic programming languages, including new language constructs, a formalism, and the first demonstration of effectiveness in practice. Instead of relying on rigid black-box inference algorithms hard-code...
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| Format: | Article |
| Sprog: | English |
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ACM
2021
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| Online adgang: | https://hdl.handle.net/1721.1/136984 |
| _version_ | 1826198383185887232 |
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| author | Mansinghka, Vikash K. Schaechtle, Ulrich Handa, Shivam Radul, Alexey Chen, Yutian Rinard, Martin |
| author2 | MIT-IBM Watson AI Lab |
| author_facet | MIT-IBM Watson AI Lab Mansinghka, Vikash K. Schaechtle, Ulrich Handa, Shivam Radul, Alexey Chen, Yutian Rinard, Martin |
| author_sort | Mansinghka, Vikash K. |
| collection | MIT |
| description | © 2018 Copyright held by the owner/author(s). We introduce inference metaprogramming for probabilistic programming languages, including new language constructs, a formalism, and the first demonstration of effectiveness in practice. Instead of relying on rigid black-box inference algorithms hard-coded into the language implementation as in previous probabilistic programming languages, inference metaprogramming enables developers to 1) dynamically decompose inference problems into subproblems, 2) apply inference tactics to subproblems, 3) alternate between incorporating new data and performing inference over existing data, and 4) explore multiple execution traces of the probabilistic program at once. Implemented tactics include gradient-based optimization, Markov chain Monte Carlo, variational inference, and sequental Monte Carlo techniques. Inference metaprogramming enables the concise expression of probabilistic models and inference algorithms across diverse fields, such as computer vision, data science, and robotics, within a single probabilistic programming language. |
| first_indexed | 2024-09-23T11:04:00Z |
| format | Article |
| id | mit-1721.1/136984 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:04:00Z |
| publishDate | 2021 |
| publisher | ACM |
| record_format | dspace |
| spelling | mit-1721.1/1369842023-04-10T20:10:40Z Probabilistic programming with programmable inference Mansinghka, Vikash K. Schaechtle, Ulrich Handa, Shivam Radul, Alexey Chen, Yutian Rinard, Martin MIT-IBM Watson AI Lab Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 2018 Copyright held by the owner/author(s). We introduce inference metaprogramming for probabilistic programming languages, including new language constructs, a formalism, and the first demonstration of effectiveness in practice. Instead of relying on rigid black-box inference algorithms hard-coded into the language implementation as in previous probabilistic programming languages, inference metaprogramming enables developers to 1) dynamically decompose inference problems into subproblems, 2) apply inference tactics to subproblems, 3) alternate between incorporating new data and performing inference over existing data, and 4) explore multiple execution traces of the probabilistic program at once. Implemented tactics include gradient-based optimization, Markov chain Monte Carlo, variational inference, and sequental Monte Carlo techniques. Inference metaprogramming enables the concise expression of probabilistic models and inference algorithms across diverse fields, such as computer vision, data science, and robotics, within a single probabilistic programming language. 2021-11-01T16:59:51Z 2021-11-01T16:59:51Z 2018-06-11 2019-07-02T16:39:26Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/136984 Mansinghka, Vikash K., Schaechtle, Ulrich, Handa, Shivam, Radul, Alexey, Chen, Yutian et al. 2018. "Probabilistic programming with programmable inference." en 10.1145/3192366.3192409 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ACM Other repository |
| spellingShingle | Mansinghka, Vikash K. Schaechtle, Ulrich Handa, Shivam Radul, Alexey Chen, Yutian Rinard, Martin Probabilistic programming with programmable inference |
| title | Probabilistic programming with programmable inference |
| title_full | Probabilistic programming with programmable inference |
| title_fullStr | Probabilistic programming with programmable inference |
| title_full_unstemmed | Probabilistic programming with programmable inference |
| title_short | Probabilistic programming with programmable inference |
| title_sort | probabilistic programming with programmable inference |
| url | https://hdl.handle.net/1721.1/136984 |
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