Grant: Canada Research Chairs
University of Regina
$100,000.00
Computational Learning Theory
EF 2021-22
Résumé
Machine learning often requires large amounts of potentially expensive data, a problem that is addressed by research on interactive learning. While classical machine learning models assume either randomly selected data or extremely inadequate data, interactive models assume carefully selected data, as is reasonable in a variety of applications. Dr. Zilles¿ objective is to design and analyze formal models of interactive learning and to develop data-economical algorithms that can efficiently solve complex learning problems. She is aiming at theoretical guarantees, but her long-term goal is to achieve improvements in applied machine learning as well.
Programme
Canada Research Chairs
Bénéficiaire
Emplacement
Saskatchewan, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
CRC-2016-00297
Name nom
Zilles, Sandra
Department département
Department of Computer Science - University of Regina
Organization id
17
Institution établissement
University of Regina
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2016
Award amount
100000
Program id
CRC
Program name en
Canada Research Chairs
Program name fr
Chaires de recherche du Canada
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
9998
Committee name en
Canada Research Chair Selection Committee
Committee name fr
Comité de sélection des Chaires de recherche du Canada
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Installment
inst:5-1
Partie
1
Nb partie
5
Application title
Computational Learning Theory
Keyword
NSERC
Application summary
Machine learning often requires large amounts of potentially expensive data, a problem that is addressed by research on interactive learning. While classical machine learning models assume either randomly selected data or extremely inadequate data, interactive models assume carefully selected data, as is reasonable in a variety of applications. Dr. Zilles¿ objective is to design and analyze formal models of interactive learning and to develop data-economical algorithms that can efficiently solve complex learning problems. She is aiming at theoretical guarantees, but her long-term goal is to achieve improvements in applied machine learning as well.
