Uploaded October 2021 | Updated September 2026, 2 weeks ago
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 physicians with decision support tools based on big health data. The second subject Uri is interested in is the intersection of machine learning and causal inference, especially the problem of learning individual-level effects. Finally, Uri is working on bringing ideas from causal inference into the field of machine learning, focusing on problems in robust learning, transfer learning and interpretability.
Previously, Uri was a postdoctoral researcher in Prof. David Sontag’s Clinical Machine Learning Lab in NYU and then MIT. He completed his PhD studies at the Center for Neural Computation at The Hebrew University of Jerusalem, under the guidance of Prof. Gal Chechik and Prof. Daphna Weinshall.
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 physicians with decision support tools based on big health data. The second subject Uri is interested in is the intersection of machine learning and causal inference, especially the problem of learning individual-level effects. Finally, Uri is working on bringing ideas from causal inference into the field of machine learning, focusing on problems in robust learning, transfer learning and interpretability.
Previously, Uri was a postdoctoral researcher in Prof. David Sontag’s Clinical Machine Learning Lab in NYU and then MIT. He completed his PhD studies at the Center for Neural Computation at The Hebrew University of Jerusalem, under the guidance of Prof. Gal Chechik and Prof. Daphna Weinshall.
