The focus of the CHIL group is to make classifiers and other Machine Learning (ML) artefacts easy to create, update, and transfer with little ML expertise and negligible cost.
Group Contact: firstname.lastname@example.org
- ICE: Interative Classification and Entity Extraction
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The system leverages big data to find examples that maximize the training value of its interaction with the teacher.
- Denis Charles
- David Grangier
Simard, P., Chickering, D., Lakshmiratan, A., Charles, D., Bottou, L., Suarez, C.G.J., Grangier,D., Amershi,S., Verwey,J., Suh,J.: Ice: Enabling non-experts to build models interactively for large-scale lopsided problems. arXiv:1409.4814 [cs.AI], Microsoft Research (2014)
- Saleema Amershi, Max Chickering, Steven M. Drucker, Bongshin Lee, Patrice Simard, and Jina Suh, ModelTracker: Redesigning Performance Analysis Tools for Machine Learning, in Proceedings of the Conference on Human Factors in Computing Systems (CHI 2015), ACM – Association for Computing Machinery, April 2015.
- Jason D. Williams, Nobal B. Niraula, Pradeep Dasigi, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez, Mouni Reddy, and Geoff Zweig, Rapidly scaling dialog systems with interactive learning, 11 January 2015.
- Saleema Amershi, Maya Cakmak, W. Bradley Knox, and Todd Kulesza, Power to the People: The Role of Humans in Interactive Machine Learning, in AI Magazine, AAAI - Association for the Advancement of Artificial Intelligence, December 2014.
- Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, and Denis Charles, Structured Labeling for Facilitating Concept Evolution in Machine Learning , in Proceedings of the Conference on Human Factors in Computing Systems (CHI 2014), ACM, May 2014.