Call for Papers
Call for Papers
We invite the submission of short papers, up to 4 pages (excluding reference and supplementary material). Papers should be in ICML 2019 format (see the official style guidelines). Submissions will be accepted as contributed talks or poster presentations. Accepted papers will be posted on the website but there will not be archival proceedings.
Paper submission: OpenReview (the review process is double-blind).
Relevant topics include but are not limited to:
Algorithms & Theory
- Novel algorithms for adaptive and multitask learning (e.g., for learning context-aware or personalized models).
- Understanding meta-learning, multitask learning, never-ending learning, few-shot learning, learning personalized models.
- Understanding the capacity of different models in the context of multi-task learning.
- Understanding per-task data efficiency and sample complexity.
- Theoretical guarantees for transfer learning, domain adaptation, etc.
- Learning representations that enable efficient multitask learning.
- Learning algorithms, agents, and systems in dynamic/complex environments.
Algorithms & Systems
- Scalable approaches to multitask learning and adaptation.
- Distributed and federated learning algorithms and systems.
- Pre-training and fine-tuning approaches and best practices.
- Weak supervision and automated data labeling.
- Modular AI/ML algorithms and systems.
- Applications of adaptive and multitask algorithms in recommender, robotic, and healthcare systems.
- Real-world problems and benchmarks for few-shot learning.
Additionally, we welcome and encourage position papers under this workshop theme. We are also particularly interested in papers that introduce benchmark datasets, challenges, and competitions to further the progress of the field.
- Submission deadline (extended): May 14, 2019
- Author notification: May 25, 2019
- Final version: June 13, 2019
- Workshop: June 15, 2019