Taming discrete integration via the boon of dimensionality

Jeffrey M. Dudek, Dror Fried, Kuldeep S. Meel

نتاج البحث: نشر في مجلةمقالة من مؤنمرمراجعة النظراء


Discrete integration is a fundamental problem in computer science that concerns the computation of discrete sums over exponentially large sets. Despite intense interest from researchers for over three decades, the design of scalable techniques for computing estimates with rigorous guarantees for discrete integration remains the holy grail. The key contribution of this work addresses this scalability challenge via an efficient reduction of discrete integration to model counting. The proposed reduction is achieved via a significant increase in the dimensionality that, contrary to conventional wisdom, leads to solving an instance of the relatively simpler problem of model counting. Building on the promising approach proposed by Chakraborty et al [9], our work overcomes the key weakness of their approach: a restriction to dyadic weights. We augment our proposed reduction, called DeWeight, with a state of the art efficient approximate model counter and perform detailed empirical analysis over benchmarks arising from neural network verification domains, an emerging application area of critical importance. DeWeight, to the best of our knowledge, is the first technique to compute estimates with provable guarantees for this class of benchmarks.

اللغة الأصليةالإنجليزيّة
دوريةAdvances in Neural Information Processing Systems
مستوى الصوت2020-December
حالة النشرنُشِر - 2020
الحدث34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
المدة: ٦ ديسمبر ٢٠٢٠١٢ ديسمبر ٢٠٢٠

ملاحظة ببليوغرافية

Publisher Copyright:
© 2020 Neural information processing systems foundation. All rights reserved.


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