{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:57:32Z","timestamp":1777129052686,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1272","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"11104-11108","source":"Crossref","is-referenced-by-count":1,"title":["PyTorch-Lifestream: Learning Embeddings on Discrete Event Sequences"],"prefix":"10.24963","author":[{"given":"Artem","family":"Sakhno","sequence":"first","affiliation":[{"name":"Sber AI Lab"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Kireev","sequence":"additional","affiliation":[{"name":"Sber AI Lab"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitrii","family":"Babaev","sequence":"additional","affiliation":[{"name":"SaluteDevices"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxim","family":"Savchenko","sequence":"additional","affiliation":[{"name":"Sber AI Lab"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gleb","family":"Gusev","sequence":"additional","affiliation":[{"name":"Sber AI Lab"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrey","family":"Savchenko","sequence":"additional","affiliation":[{"name":"Sber AI Lab"},{"name":"ISP RAS Research Center for Trusted Artificial Intelligence"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:38Z","timestamp":1758627398000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1272"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1272","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}