Advances in Set Function Learning: A Survey of Techniques and Applications
Date
2025-02-21
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ACM Computing Surveys
Abstract
Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. Unlike traditional machine learning that involves fixed-size input vectors where the order of features matters, set function learning demands methods that are invariant to permutations of the input set, presenting a unique and complex problem. This survey provides a comprehensive overview of the current development in set function learning, covering foundational theories, key methodologies, and diverse applications. We categorize and discuss existing approaches, focusing on deep learning approaches, such as DeepSets and Set Transformer-based methods, as well as other notable alternative methods beyond deep learning, offering a complete view of current models. We also introduce various applications and relevant datasets, such as point cloud processing and multi-label classification, highlighting the significant progress achieved by set function learning methods in these domains. Finally, we conclude by summarizing the current state of set function learning approaches and identifying promising future research directions, aiming to guide and inspire further advancements in this promising field.
Description
This article was originally published in ACM Computing Surveys. The version of record is available at: https://doi.org/10.1145/3715905.
© 2025 Copyright held by the owner/author(s).
This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0).
Keywords
computing methodologies, supervised learning, neural networks, artificial intelligence, set function learning, deep learning, permutation-invariance, pooling, aggregation
Citation
Xie, Jiahao, and Guangmo Tong. “Advances in Set Function Learning: A Survey of Techniques and Applications.” ACM Computing Surveys 57, no. 7 (July 31, 2025): 1–37. https://doi.org/10.1145/3715905.