Record link: 
http://hdl.handle.net/20.500.12708/227802
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Title: 
DeGAS: Gradient-Based Optimization of Probabilistic Programs without Sampling
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Citation: 
Randone, F., Doz, R., Tribastone, M., & Bortolussi, L. (2026). DeGAS: Gradient-Based Optimization of Probabilistic Programs without Sampling. In Tools and Algorithms for the Construction and Analysis of Systems (pp. 566–585). https://doi.org/10.1007/978-3-032-22752-2_29
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Publisher DOI: 
10.1007/978-3-032-22752-2_29
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Randone, Francesca 
Doz, Romina 
Tribastone, Mirco 
Bortolussi, Luca 
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Organisational Unit: 
E191-01 - Forschungsbereich Cyber-Physical Systems 
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Published in: 
Tools and Algorithms for the Construction and Analysis of Systems 
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ISBN: 
978-3-032-22752-2
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Volume: 
16505
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Date (published): 
2026
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Event name: 
32nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2026) 
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Event date: 
13-Apr-2026 - 16-Apr-2026
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Event place: 
Italy
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Number of Pages: 
20
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Peer reviewed: 
Yes
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Keywords: 
Differentiable semantics; Gaussian approximate semantics; Gaussian mixture semantics
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Probabilistic programming; Loopless probabilistic programs
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Project title: 
Exploring Moment-Based Equivalence for probabilistic programs: 101205923 (European Commission) 
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Conference Paper

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