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Combining multiple imputation and propensity score matching in practice

10 septembre 2024 @ 0 h 00 min – 23 h 59 min –

Room 02.124, Building 5, St Priest campus

Machine Learning in Montpellier, Theory & Practice

Causal inference using observational data presents many statistical challenges, particularly when dealing with missing confounder data. While multiple imputation offers a potential solution, its implementation with propensity score matching requires careful consideration. In this talk, we will delve into empirical studies conducted by the LSHTM* Statistics team to explore these matters.,,

Machine Learning in Montpellier, Theory & Practice

Over-parameterisation and Overfitting: Myths, Theories and Tools

10 septembre 2024 @ 14 h 00 min –

Room 02.124, Building 5, St Priest campus

Machine Learning in Montpellier, Theory & Practice

Overfitting on training data is typically assumed to be a bad practice. However, modern machine learning models are highly over-parameterised, to the extent that they can overfit on training data. In this talk, I will discuss new theories that debunk the myths that: (1) large models with too many parameters always overfit the training data; and (2) models that perfectly fit the training data cannot predict well on new data.I will then present our recent works on generalisation and learning dynamics of over-parameterised models, including (1) the double descent phenomenon in causal inference and (2) the neural tangent kernel approximation for semi- and self-supervised models. I will highlight how some of our results resolve conjectures in machine learning, and also provide new practical tools.,,

Machine Learning in Montpellier, Theory & Practice