Fast Generating A Large Number of Gumbel-Max Variables

Qi, Yiyan; Wang, Pinghui; Zhang, Yuanming; Zhao, Junzhou; Tian, Guangjian; Guan, Xiaohong

doi:10.1145/3366423.3380160

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Therefore, the same sampling process may give rise to different distributions, depending on what is considered 'the sample'. When repeated samples from the categorical are drawn using the Gumbel-max trick, it may be possible to gain efficiency by reusing computations, as proposed by the authors of [107], [125].…”

Section: Unstructured Distributionsmentioning

confidence: 99%

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

Huijben

Kool

Paulus³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Section: Unstructured Distributionsmentioning

confidence: 99%

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

Huijben

Kool

Paulus³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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“…They also demonstrated that the probability Jaccard similarity is scale invariant and more sensitive to changes in vectors. Qi et al [40] proposed FastGM to further reduce the time complexity of P-MinHash by generating the hash values in order.…”

Section: Related Workmentioning

confidence: 99%

Generalized Sketches for Streaming Sets

Guo

et al. 2022

Applied Sciences

Self Cite

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Many real-world datasets are given as a stream of user–interest pairs, where a user–interest pair represents a link from a user (e.g., a network host) to an interest (e.g., a website), and may appear more than once in the stream. Monitoring and mining statistics, including cardinality, intersection cardinality, and Jaccard similarity of users’ interest sets on high-speed streams, are widely employed by applications such as network anomaly detection. Although estimating set cardinality, set intersection cardinality, and set Jaccard similarity, respectively, is well studied, there is no effective method that provides a one-shot solution for estimating all these three statistics. To solve the above challenge, we develop a novel framework, SimCar. SimCar online builds an order-hashing (OH) sketch for each user occurring in the data stream of interest. At any time of interest, one can query the cardinalities, intersection cardinalities, and Jaccard similarities of users’ interest sets. Specially, using OH sketches, we develop maximum likelihood estimation (MLE) methods to estimate cardinalities and intersection cardinalities of users’ interest sets. In addition, we use OH sketches to estimate Jaccard similarities of users’ interest sets and build locality-sensitive hashing tables to search for users with similar interests with sub-linear time. We evaluate the performance of our methods on real-world datasets. The experimental results demonstrate the superiority of our methods.

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“…When repeated samples from the categorical are drawn using the Gumbel-max trick, it may be possible to gain efficiency by reusing computations, as proposed by the authors of [104], [122].…”

Section: Structured Distributionmentioning

confidence: 99%

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

Huijben¹,

Kool²,

Paulus³

et al. 2021

Preprint

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The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, e.g., drawing multiple samples, sampling from structured domains, or gradient estimation for error backpropagation in neural network optimization. The goal of this survey article is to present background about the Gumbel-max trick, and to provide a structured overview of its extensions to ease algorithm selection. Moreover, it presents a comprehensive outline of (machine learning) literature in which Gumbel-based algorithms have been leveraged, reviews commonly-made design choices, and sketches a future perspective.

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Fast Generating A Large Number of Gumbel-Max Variables

Cited by 5 publications

References 42 publications

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

Generalized Sketches for Streaming Sets

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

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