We would like to invite you to our April event, where we are very happy to have Prof Benoit Liquet-Weiland and Dr Sarat Babu Moka from Macquarie University give a talk about Best Subset Selection via Continuous Optimization.

This meeting is a face-to-face meeting but if you would like to attend virtually please register in advance for this lecture here, you will receive a confirmation email containing information about joining the meeting after registering.

Any questions, please feel free to contact:  secretary.nswbranch@statsoc.org.au.

Date: Wednesday, 27th April 2022

Time:
6:00pm - 6:30pm: Refreshments
6:30pm - 7:30pm: Lecture
7:45pm onwards: Dinner (at a nearby restaurant).
Please RSVP for dinner before 22th April 2022 to attend.

Venue: F10A.01.104.Law Building Annex.Law Annex Lecture Theatre 104

Prof Benoit Liquet-Weiland and Dr Sarat Babu Moka -  Macquarie University

Title: Best Subset Selection via Continuous Optimization

Abstract:

Recent rapid developments in information technology have enabled 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. Several optimization and search methods have been proposed in the literature to tackle the problem of identifying or ‘selecting’ the set of important predictors. These methods include forward stepwise, Lasso, and mixed-integer optimization.

In this talk, we will briefly review existing methods, and then present a $L_0$ continuous optimization based solution, a novel approach that tackles the challenging task of best subset selection for linear models, especially when the number of features is very large. Simulation results are presented to highlight the performance of the proposed method in comparison to the existing methods. Our new formulation for best subset selection in linear regression models promises to open new research avenues for feature extraction for a large variety of models.

Biography:

Dr Liquet  is Professor of Mathematical and Computational Statistics at Macquarie University in the Department of Mathematics and Statistics. In addition he is affiliated to the University of Queensland and to the Université de Pau et Pays de l’Adour (UPPA). He was previously affiliated with ACEMS (Centre of Excellence for Mathematical and Statistical Frontiers), Queensland University of Technology. He was a senior lecturer in Statistics at The University of Queensland (from 2013-2015), Senior Investigator Statistician at Medical Research Council Biostatistics Unit in Cambridge (from 2012-2013), Associate Professor at Bordeaux University (from 2017-2012). Throughout his career he has extensively worked in developing novel statistical models mainly to provide novel tools to analyse clinical, health and biological data arising from epidemiological studies. Since 2011, he moved to the field of computational biology and generalised some of these methods so that they scale to high throughput (“omic”) data.  He has been teaching an advanced course on the mathematical engineering of Deep Learning at the Australian Mathematical Sciences Institute (AMSI) summer school in 2021.  A book  draft of his new co-authored book on concepts of “Deep Learning” is available at https://deeplearningmath.org . Benoit Liquet works on Applied Statistics, as well as on the development of R packages and on industrial applications (such as Machine Learning). 

Dr Sarat Moka is a Research Fellow in the School of Mathematical and Physical Sciences, Macquarie University. He was previously an ACEMS (ARC Centre for Excellence for Mathematical & Statistical Frontiers) Postdoc at The University of Queensland. He has obtained a PhD in Applied Probability from Tata Institute of Fundamental Research, Mumbai, and Master of Engineering from Indian Institute of Science, Bangaloru. He has been working on problems related to exact sampling and exact estimation for Spatial Point Processes, Graph Models, and Diffusions. In addition, he has worked on modelling of cancer data and on Safe Blues project (https://safeblues.org/).  His current research focus includes problems in Statistics, particularly Model Selection. He has been teaching advanced courses in Statistics and Deep Learning and is currently co-writing a book on "The Mathematical Engineering of Deep Learning" (https://deeplearningmath.org/).