Agenda

Title: 
Prevalidated ridge regression as a highly-efficient drop-in replacement for logistic regression for high-dimensional data

Abstract:
Linear models are widely used in classification, and are particularly effective for high-dimensional data where linear decision boundaries / separating hyperplanes are often effective for separating classes, even for complex data. A recent example of a technique effectively utilising linear classifiers is the ROCKET family of classifiers for time series classification. One reason that the ROCKET family is so fast is due to its use of a linear classifier based around standard squared-error ridge regression. Fitting a linear model based on squared-error is significantly faster and more stable than fitting a standard regularised multinomial logistic regression based on logarithmic-loss (i.e., regularised maximum likelihood), as in the latter case the solutions can only be found via a numerical search.

While fast, one drawback of using squared-error ridge-regression is that it is unable to produce probabilistic predictions. I will demonstrate some very recent work on how to use regular ridge-regression to train L2-regularized multinomial logistic regression models for very large numbers of features, including choosing a suitable degree of regularization, with a time complexity that is no greater than single ordinary least-squares fit. This in contrast to logistic regression, which requires a full refit for every value of regularisation parameter considered, and every fold used for cross-validation. Using our new approach allows for models based on linear classifier technology to provide well calibrated probabilistic predictions with minimal additional computational overhead. If time permits, I will also discuss some thoughts on when such linear classifiers would be expected to perform well.

Bio: 
Daniel Schmidt is an Associate Professor of Computer Science at the Department of Data Science and AI, Monash University, Australia. He obtained his PhD in the area of information theoretic statistical inference in 2008 from Monash University. From 2009 to 2018 he was employed at the University of Melbourne, working in the field of statistical genomics (GWAS, epigenetics and cancer). Since 2018 he has been employed at Monash University in a teaching and research position. His research interests are primarily time series classification and forecasting, particularly at scale, and Bayesian inference and MCMC, with an emphasis on sparsity and shrinkage, Bayesian optimisation and Bayesian function approximation. He also has a keen interest in the best ways to provide statistical/machine learning education.
 
 
URL: 
https://oficinavirtual.ugr.es/redes/SOR/SALVEUGR/accesosala.jsp?IDSALA=22978479 
 
 
Password: 
752996

Title: 
The role of artificial intelligence in achieving the Sustainable Development Goals

Abstract: 
The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.
 
Bio:
Dr. Ricardo Vinuesa is an Associate Professor at the Department of Engineering Mechanics, KTH Royal Institute of Technology in Stockholm. He is also Vice Director of the KTH Digitalization Platform and Lead Faculty at the KTH Climate Action Centre. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain), and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand, control and predict complex wall-bounded turbulent flows, such as the boundary layers developing around wings and urban environments. Dr. Vinuesa has received, among others, an ERC Consolidator Grant (the most prestigious research program in Europe), the TSFP Kasagi Award,  the Goran Gustafsson Award for Young Researchers, the IIT Outstanding Young Alumnus Award, the SARES Young Researcher Award and he leads several large Horizon Europe projects. He is also a member of the Young Academy of Science of Spain.
 
URL:
https://oficinavirtual.ugr.es/redes/SOR/SALVEUGR/accesosala.jsp?IDSALA=22978478
 
Password:
934189