Data-Efficient Machine Learning
Topics of interest include, but are not limited toSemi-supervised and Self-supervised Learning
Active Methods: active learning, bandit techniques
Learning from Similar Tasks: transfer learning, multi-task learning, meta-learning, domain adaptation
Crowdsourcing: human annotation methods, design of experiments
Synthetic data: data augmentation, adversarial data generation
Given the data mining focus of this conference, we will also consider, although not limit to, the following application domains.Recommendation models: recommender systems, collaborative filtering, knowledge graphs
E-commerce: fraud and abuse mitigation, misinformation, advertising
Social media: misbehavior, sentiment analysis, cyberbullying
Information retrieval: web search, ranking
Time Series Analysis
Submission Guidelines
Authors are invited to submit papers of 2-8 pages in length. Papers should be submitted electronically in PDF format, using the ACM SIG Proceedings format, with a font size no smaller than 9pt. Submit papers through EasyChair. All submissions will be single blind and peer-reviewed. Each submission will be reviewed by at least 3 members of the PC. Papers will be evaluated according to their significance, originality, technical content, style, clarity, and relevance to the workshop. All accepted papers will be presented at the workshop. We encourage both academic and industry submissions of the following types, but not limited to:Novel research papers in full or short length
Work-in-progress papers
Position papers
Survey papers
Comparison papers of existing methods and tools
Case studies
Demo papers
Extended abstracts
Important Dates Paper Submission Deadline: May 10, 2021
Acceptance notification: June 10, 2021
Camera-ready due: June 25, 2021
Publication of workshop proceedings: July 2, 2021
Date of workshop: Between 14-18 August, 2021