3 papers
cs.LG2026
Bridging the Divide: End-to-End Sequence-Graph Learning
Yuen Chen, Yulun Wu, Samuel Sharpe +5
Many real-world prediction tasks, particularly those involving entities such as customers or patients, involve both {sequential} and {relational} data. Each entity maintains its ow…
cs.IR2026
Revisiting RAG Retrievers: An Information Theoretic Benchmark
Wenqing Zheng, Dmitri Kalaev, Noah Fatsi +5
Retrieval-Augmented Generation (RAG) systems rely critically on the retriever module to surface relevant context for large language models. Although numerous retrievers have recent…
cs.LG2025
Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
Rizal Fathony, Igor Melnyk, Owen Reinert +3
User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains. User ev…