RelKD 2026: The Fourth International Workshop on Resource-Efficient Learning for Knowledge Discovery

Abstract

Modern machine learning techniques, particularly deep learning, have shown remarkable efficacy in numerous knowledge discovery and data mining applications. However, the advancement of these methods is frequently impeded by resource constraint challenges in many scenarios, such as limited labeled data (data-level), small model size requirements in real-world computing platforms (model-level), and efficient mapping of the computations to heterogeneous target hardware (system-level). Addressing all these factors is crucial for effectively and efficiently deploying developed models across a broad spectrum of real-world systems, including large-scale social network analysis, recommendation systems, and real-time anomaly detection. Therefore, there is a critical need to develop efficient learning techniques to address the challenges posed by resource limitations, whether from data, model/algorithm, or system/hardware perspectives. The proposed fourth international workshop on ''Resource-Efficient Learning for Knowledge Discovery (RelKD 2026)'' will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities for resource-efficient learning.