Connectionists: Invitation to contribute to Frontiers in Big Data journal

[Hsuan-Tien Lin]

Dear colleagues on the connectionists mailing list,

We invite you and your co-authors to publish your next article on the
research topic: Towards More Reliable and Sustainable Machine Learning
Services in the Frontiers in Big Data journal.

Abstract Submission Deadline 03 April 2023
Manuscript Submission Deadline 19 June 2023

In the past decades, the area of Machine Learning (ML) has experienced
tremendous success. Companies have begun to rely on ML to provide
continuous services to their clients. For instance, recommender
systems and learning-to-rank models are widely used by Internet
companies to serve their customers. An important feature of a reliable
and sustainable ML based service is that it exceeds the basic training
requirements. From onset, it involves data preparation (e.g. data
ingestion, curation, validation), enhanced attention to feature
selection and engineering, and may also rely on ensemble models to
further boost the performance of the ML based service.

Since ML services are often served online for a long period of time,
issues such as auto model re-training with incremental feedback,
handling concept drift and environment changing become very critical.
Moreover, as there are costs in providing such services, how to strike
a balance among performance, computation resources, and ease of
maintenance can be very challenging.

To advance the research in this direction, we would like to solicit
articles on the following topics:
- Dealing with dynamic environment in ML handling concept drift
- Learning given incremental feedbacks
- Causality inference
- Dealing with noise and missing in data
- Dealing with sampling, measuring, and algorithmic bias in ML
- Resourced constrained machine learning
- Evaluation metrics in reliability and sustainability for ML
- Auto machine learning
- Life-long learning
- Invariant learning

More details on this special issue can be found at:

https://www.frontiersin.org/research-topics/47091/towards-more-reliable-and-sustainable-machine-learning-services

Best regards,
Bo Han, Hong Kong Baptist University
Hsuan-Tien Lin, National Taiwan University
Shou-de Lin, National Taiwan University