Sandra Zilles and her team focus on theoretical aspects of machine learning. She is particularly interested in how to model and exploit special types of interaction with machines to make them learn using less data than conventional approaches would use. Intuitively, the research will make intelligent machines exploit the quality of well-chosen data rather than requiring a large quantity of potentially expensive data. The models and algorithmic techniques that will ultimately arise from this research may provide efficient solutions to complex problems in artificial intelligence – at a lower cost and with less data than is currently possible.
- Membership in the College of New Scholars, Artists, and Scientists of the Royal Society of Canada, 2017 - 2024
- NSERC Canada Research Chair (Tier 2) in Computational Learning Theory, 2010 - 2017 and 2017 - 2022
- CACS/AIC (Canadian Association for Computer Science) Outstanding Young Researcher Award, 2013
- Alanazi, E., Mouhoub, M., & Zilles, S. (2020). The complexity of exact learning of acyclic conditional preference networks from swap examples. Artificial Intelligence, 278, 103182.
- Gao, Z., Ries, C., Simon, H. U., & Zilles, S. (2017). Preference-based teaching. The Journal of Machine Learning Research, 18(1), 1012-1043.
- Doliwa, T., Fan, G., Simon, H. U., & Zilles, S. (2014). Recursive teaching dimension, VC-dimension and sample compression. The Journal of Machine Learning Research, 15(1), 3107-3131.
- Zilles, S., Lange, S., Holte, R., Zinkevich, M., & Cesa-Bianchi, N. (2011). Models of cooperative teaching and learning. Journal of Machine Learning Research, 12(2).
- Arfaee, S. J., Zilles, S., & Holte, R. C. (2011). Learning heuristic functions for large state spaces. Artificial Intelligence, 175(16-17), 2075-2098.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations.