Modern machine learning techniques, especially deep neural networks, have demonstrated excellent performance for various knowledge discovery and data mining applications. However, the development of many of these techniques still encounters 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 of these metrics is critical for the effective and efficient usage of the developed models in a wide variety of real systems, such as large-scale social network analysis, large-scale recommendation systems, and real-time anomaly detection. Therefore, it is desirable to develop efficient learning techniques to tackle challenges of resource limitations from data, model/algorithm, or (and) system/hardware perspectives. The proposed international workshop on "Resource-Efficient Learning for Knowledge Discovery (RelKD 2025)" will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities of resource-efficient learning.

The goal of this workshop is to create a venue to tackle the challenges that arise when modern machine learning techniques (e.g., deep neural networks) encounter resource limitations (e.g., scarce labeled data, constrained computing devices, low power/energy budget). The workshop shall focus on machine learning techniques used for knowledge discovery and data science applications, with a focus on efficient learning from three angles: data, algorithm/model, and system/hardware. The topics of interest will include:

The workshop will be a half-day session comprising several invited talks from distinguished researchers in the field, spotlight lightning talks and a poster session where contributing paper presenters can discuss their work, and a concluding panel discussion focusing on future directions. Attendance is open to all registered participants.

Submitted technical papers should be at least 4 pages long. All papers must be submitted in PDF format (any template is ok). Papers will be peer-reviewed and selected for spotlight and/or poster presentation. We welcome any kinds of submissions, e.g., papers already accepted to or currently under review by other venues, ongoing studies, and so on. We will also select the Best Paper award. Submission site: