{"type":"ScholarlyArticle","title":"PyTorch-Lifestream: Learning Embeddings on Discrete Event Sequences","authors":[{"name":"Artem Sakhno","slug":"artem-sakhno","given":"Artem","family":"Sakhno","affiliation":""},{"name":"Ivan Kireev","slug":"ivan-kireev","given":"Ivan","family":"Kireev","affiliation":""},{"name":"Dmitrii Babaev","slug":"dmitrii-babaev","given":"Dmitrii","family":"Babaev","affiliation":""},{"name":"Maxim Savchenko","slug":"maxim-savchenko","given":"Maxim","family":"Savchenko","affiliation":""},{"name":"Gleb Gusev","slug":"gleb-gusev","given":"Gleb","family":"Gusev","affiliation":""},{"name":"Andrey Savchenko","slug":"andrey-savchenko","given":"Andrey","family":"Savchenko","affiliation":""}],"abstract":"The domain of event sequences is widely applied in various industrial tasks in banking, healthcare, etc., where temporal tabular data processing is required. This paper introduces PyTorch-Lifestream, the first open-source library specially designed to handle event sequences. It supports scenarios with multimodal data and offers a variety of techniques for learning embeddings of event sequences and end-to-end model training. Furthermore, PyTorch-Lifestream efficiently implements state-of-the-art methods for event sequence analysis and adapts approaches from similar domains, thus enhancing the versatility and performance of sequence-based models for a wide range of applications, including financial risk scoring, campaigning, user ID matching, churn prediction, fraud detection, medical diagnostics, and recommender systems.","doi":"10.24963/ijcai.2025/1272","url":"https://ijcai.org/proceedings/2025/1272","pdf":"https://ijcai.org/proceedings/2025/1272.pdf","markdown":"https://ijcai.org/proceedings/2025/1272.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1272","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Demo Track","track":"Demo Track","number":1272,"pages":{"first":11104,"last":11108},"keywords":["Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Benchmarks","Machine Learning: ML: Sequence and graph learning","Machine Learning: ML: Unsupervised learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}