Machine learning has achieved great success in various tasks, particularly in supervised learning tasks such as classification and regression. Typically, predictive models are learned from a training data set that contains a large amount of training examples, each corresponding to an event/object. A training example consists of two parts: a feature vector (or instance) describing the event/object, and a label indicating the ground-truth output. In classification, the label indicates the class to which the training example belongs; in regression, the label is a real-value response corresponding to the example. Most successful techniques, such as deep learning, require ground-truth labels to be given for a big training data set. In many tasks, however, it can be difficult to attain strong supervision information due to the high cost of the data-labeling process. Thus, it is highly desirable for machine learning techniques to be able to work with weak supervision data.
The aim of the workshop is to highlight the current research related to weakly supervised learning techniques in different types of weak supervision and their applications in real problems. The workshop will also emphasize a discussion for the major challenges for the future of weakly supervised learning and provide an opportunity to researchers for related fields such as optimization, statistical learning to get a feedback from other community.
The workshop will highlight a growing area of weakly supervised learning. Techniques based on this area have been studied substantially for different application areas in the previous decade. Knowledge Discovery and Data mining has been one of the fastest growing application areas of these techniques. PAKDD 2019 being a major data mining conference will enable the data mining researchers to look at the some of the high quality work presented in the workshop and the potential future benefits of these techniques. On the other hand presenters in the workshop will get an opportunity to get a critical view on their work form other community. The workshop will bring these communities together and will be helpful to increase the number of attendees of the conference and diversify the research in the future.
The research calls for high quality research papers outlining current research, literature surveys, theoretical and empirical studies, and other relevant work including but not limited to the following areas:
- Active learning
- Semi-supervised learning (including transductive learning)
- Transfer learning (including domain adaptation)
- Few-shot learning (including one-shot, zero-shot learning)
- Partial label learning
- Multi-instance learning
- Multi-instance multi-label learning
- Label distribution learning
- Noise-label learning
- New class label
- New feature set
- New objective
- Data distribution change
- Computer Vision
- Natural Language Processing
- Video/Audio Classification
- Self-paced learning
- Many others
- Long Papers: maximum twelve (12) pages, in the Springer LNAI paper templates, including bibliography and appendices.
- Short Papers: maximum six (6) pages, in the Springer LNAI paper templates, including bibliography and appendices.
- Submission site: https://easychair.org/conferences/?conf=wel19
- Paper submission must be in English.
- Review Process
- All papers will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity.
- All paper submissions will be handled electronically.
- Accepted Papers
- Accepted WeL2019 papers will be published in Springer's Lecture Notes in Artificial Intelligence (LNAI) series, which is indexed by EI Compendex, ISI Proceedings, and Scopus.