Abstract
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count or binary) data. Recent deep probabilistic models based on variational autoencoders (VAE) have shown promising results on discrete data but may have inferior modelling performance due to the insufficient capability in modelling overdispersion and model misspecification. To address these issues, we develop a VAE-based framework using the negative binomial distribution as the data distribution. We also provide an analysis of its properties vis-à-vis other models. We conduct extensive experiments on three problems from discrete data analysis: text analysis/topic modelling, collaborative filtering, and multi-label learning. Our models outperform state-of-the-art approaches on these problems, while also capturing the phenomenon of overdispersion more effectively.
Original language | English |
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Title of host publication | Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics |
Editors | Silvia Chiappa, Roberto Calandra |
Publisher | Proceedings of Machine Learning Research (PMLR) |
Pages | 1684-1694 |
Number of pages | 10 |
Volume | 108 |
Publication status | Published - 2020 |
Event | International Conference on Artificial Intelligence and Statistics 2020 - Virtual, Italy Duration: 3 Jun 2020 → 5 Jun 2020 Conference number: 23rd https://www.aistats.org/ (Website) http://proceedings.mlr.press/v108/ (Proceedings) |
Conference
Conference | International Conference on Artificial Intelligence and Statistics 2020 |
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Abbreviated title | AISTATS 2020 |
Country/Territory | Italy |
City | Virtual |
Period | 3/06/20 → 5/06/20 |
Internet address |
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