Corruptions of Supervised Learning Problems: Typology and Mitigations
Iacovissi, Laura, Nan Lu, and Robert C. Williamson. 2024. “Corruptions of Supervised Learning Problems: Typology and Mitigations”. ArXiv.
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Slow Science
Machine learning research is notoriously fast paced. Can one get off the treadmill and embrace a notion of slow science ? I am pleased to be able to argue yes by example! Recently I have had two works published with a very long gestation. The first Geometry and Calculus of Losses reports work that...
Data Models With Two Manifestations of Imprecision
Frölich, Christian, and Robert C. Williamson. 2024. “Data Models With Two Manifestations of Imprecision”. ArXiv.
Geometry and Stability of Supervised Learning Problems
Mémoli, Facundo, Brantley Vose, and Robert C. Williamson. 2024. “Geometry and Stability of Supervised Learning Problems”. ArXiv.
Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret
Derr, Rabanus, and Robert C. Williamson. 2024. “Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret.”
Teaching
(Summer 2024) A Zoo of Non-Standard Learning Problems In the block seminar "A Zoo of Nonstandard Learning Problems" we will explore less-known learning problems. In the vast landscape of machine learning, many problems often remain overshadowed by mainstream research, despite their practical...
Bob's Inaugural Lecture
Bob Williamson gave his inaugural lecture at the University of Tübingen recently. The photo shows the FMLS group at the reception. A video recording of the lecture can be found here . A pdf of the slides is here .
Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates
Fröhlich, Christian, and Robert C. Williamson. 2023. “Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates”. ArXiv.
Systems of Precision - Intersectionality meets Measurability
The usual way probability theory is used is that you posit an algebra of events. Each such event has a probability (that is, it is "measurable"). The assumption that the set of events that have a probability is an algebra means that if A and B are both events then "A and B" is also an event (the...