Learning Neural Set Functions Under the Optimal Subset Oracle

• URL: http://arxiv.org/abs/2203.01693v4
• Date: Tue, 23 May 2023 09:16:35 GMT
• Title: Learning Neural Set Functions Under the Optimal Subset Oracle
• Authors: Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian
• Abstract summary: We present a principled yet practical maximum likelihood learning framework, termed as EquiVSet. Our framework meets the following desideratas of learning set functions under the OS oracle. Thanks to the elegant combination of these advanced architectures, empirical studies on three real-world applications demonstrate that EquiVSet outperforms the baselines by a large margin.
• Score: 48.20868958542155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority of existing works study methodologies of set function learning under the function value oracle, which, however, requires expensive supervision signals. This renders it impractical for applications with only weak supervisions under the Optimal Subset (OS) oracle, the study of which is surprisingly overlooked. In this work, we present a principled yet practical maximum likelihood learning framework, termed as EquiVSet, that simultaneously meets the following desiderata of learning set functions under the OS oracle: i) permutation invariance of the set mass function being modeled; ii) permission of varying ground set; iii) minimum prior; and iv) scalability. The main components of our framework involve: an energy-based treatment of the set mass function, DeepSet-style architectures to handle permutation invariance, mean-field variational inference, and its amortized variants. Thanks to the elegant combination of these advanced architectures, empirical studies on three real-world applications (including Amazon product recommendation, set anomaly detection, and compound selection for virtual screening) demonstrate that EquiVSet outperforms the baselines by a large margin.

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