Speaker: Benoit Liquet-Weiland
Affiliation: Macquarie University


Recent rapid developments in information technology have enabled the collection of high-dimensional complex data, including in engineering, economics, finance, biology, and health sciences. High-dimensional means that the number of features is large and often far higher than the number of collected data samples. In many of these applications, it is desirable to find a small best subset of predictors so that the resulting model has desirable prediction accuracy. In this talk, we present the COMBSS framework, a continuous optimization-based solution that we recently showed to solve the best subset selection problem in linear regression. Then, we highlight how COMBSS can be extended to other models such as the logistic model. Finally, we present how to cast the best subset solution method into principal component analysis and partial least square frameworks.

About Statistics, modelling and operations research seminars

Students, staff and visitors to UQ are welcome to attend our regular seminars.

The events are jointly run by our Operations research and Statistics and probability research groups.

The Statistics, modelling and operations research (SMOR) Seminar series seeks to celebrate and disseminate research and developments across the broad spectrum of quantitative sciences. The SMOR series provides a platform for communication of both theoretical and practical developments, as well as interdisciplinary topics relating to applied mathematics and statistics.


Priestley Building (67)
Room 442 (and via Zoom: