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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Approximate Inference via Weighted Rademacher Complexity

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Jonathan Kuck

Stanford University


Ashish Sabharwal

Allen Institute for Artificial Intelligence


Stefano Ermon

Stanford University


DOI:

10.1609/aaai.v32i1.12127


Abstract:

Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce a new technique for estimating the size of an arbitrary weighted set, defined as the sum of weights of all elements in the set. Our technique provides upper and lower bounds on a novel generalization of Rademacher complexity to the weighted setting in terms of the weighted set size. This generalizes Massart’s Lemma, a known upper bound on the Rademacher complexity in terms of the unweighted set size. We show that the weighted Rademacher complexity can be estimated by solving a randomly perturbed optimization problem, allowing us to derive high probability bounds on the size of any weighted set. We apply our method to the problems of calculating the partition function of an Ising model and computing propositional model counts (#SAT). Our experiments demonstrate that we can produce tighter bounds than competing methods in both the weighted and unweighted settings.

Topics: AAAI

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HOW TO CITE:

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon Approximate Inference via Weighted Rademacher Complexity Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon Approximate Inference via Weighted Rademacher Complexity AAAI 2018, .

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon (2018). Approximate Inference via Weighted Rademacher Complexity. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon. Approximate Inference via Weighted Rademacher Complexity. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon. 2018. Approximate Inference via Weighted Rademacher Complexity. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon. (2018) "Approximate Inference via Weighted Rademacher Complexity", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon, "Approximate Inference via Weighted Rademacher Complexity", AAAI, p., 2018.

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon. "Approximate Inference via Weighted Rademacher Complexity". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon. "Approximate Inference via Weighted Rademacher Complexity". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Jonathan Kuck||Ashish Sabharwal||Stefano Ermon. Approximate Inference via Weighted Rademacher Complexity. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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