Guest
Uri Shalit Technion – Israel Institute of Technology Title: Causality-inspired machine learning Abstract: We will discuss two recent projects where ideas from causal inference have inspired us to find new approaches to problems in machine learning. First, we show how using the idea of independence of cause and mechanism (ICM) can be used to help learn predictive models that are stable against a-priori unknown distributional shifts. Then we will present recent work where we show how a robust notion of model calibration ties into learning models that generalize well out-of-domain in both theory and practice. Speaker Bio: Uri Shalit is a senior lecturer (assistant professor) at the Technion - Israel Institute of Technology, Faculty of Industrial Engineering and Management, in the areas of statistics and information systems. Uri's research is currently focused on three subjects: The first is applying machine learning to the field of healthcare, especially in terms of providing ph
