Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations (bibtex)
by Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari and Guy Van den Broeck
Abstract:
sponsorship: The authors would like to thank Arthur Choi for several insightful discussions about the RCR framework. This work is partially supported by NSF grants #IIS-1943641, #IIS-1633857,#CCF-1837129, DARPA grant #N66001-17-2-4032, a Sloan Fellowship, Intel, and Facebook. This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. [694980] SYNTH: Synthesising Inductive Data Models). (NSF|IIS-1943641, NSF|IIS-1633857, NSF|CCF-1837129, DARPA|N66001-17-2-4032, Sloan Fellowship, Intel, European Research Council (ERC) under the European Union|694980, Facebook)
Reference:
Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari and Guy Van den Broeck. Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations, In Advances in Neural Information Processing Systems 33 (NeurIPS), 2020.
Bibtex Entry:
@inproceedings{ZengNeurIPS20,
title = {Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations},
author = {Zeng, Zhe and Morettin, Paolo and Yan, Fanqi and Vergari, Antonio and Van den Broeck, Guy},
booktitle = {Advances in Neural Information Processing Systems 33 (NeurIPS)},
month = 12,
year = {2020},
url = "http://starai.cs.ucla.edu/papers/ZengNeurIPS20.pdf",
slides = "http://starai.cs.ucla.edu/slides/ZengNeurips20.pdf",
video = "https://slideslive.com/38938005/probabilistic-inference-with-algebraic-constraints-theoretical-limits-and-practical-approximations",
code = "https://github.com/UCLA-StarAI/recoin",
keywords = {conference,selective},
annotation = "(Oral spotlight presentation, acceptance rate 385/9454 = 4.1\%)"
}PDF Preview:
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