ETH-FDS seminar series

On this website you can find information about upcoming and past seminar talks.

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Date / Time

Speaker

Title

Location

28 August 2025
15:15-16:15

John Duchi
Stanford University

Details

Title	On labels in supervised learning problems
Speaker, Affiliation	John Duchi , Stanford University
Date, Time	28 August 2025, 15:15-16:15
Location	HG D 1.2
Abstract	When we teach statistics and machine learning, we typically imagine problems in which we wish to predict some target Y from data X, or to build understanding of the relationship between these two variables, or to test some predicted effect of intervening between them. We fit models based on samples of these pairs. Yet we rarely investigate precisely where our labeled data comes from, referring instead to labels (Y) in supervised learning problems as "gold-standard" feedback, or something similar. Yet these labels are constructed via sophisticated pipelines, aggregating expert (or non-expert) feedback, combining observations in sophisticated ways, and we do not model these choices in our statistical learning pipelines. In this talk, I will discuss some work we have been doing to try to open up this bigger picture of statistics, providing some food for thought about how we might move beyond our standard statistical analyses.

On labels in supervised learning problemsread_more

23 October 2025
16:15-17:15

Tilmann Gneiting
HITS, Heidelberg

Details

Title	Assessing Monotone Dependence
Speaker, Affiliation	Tilmann Gneiting, HITS, Heidelberg
Date, Time	23 October 2025, 16:15-17:15
Location	HG E 5
Abstract	The assessment of monotone dependence between random variables $X$ and $Y$ is a classical problem in statistics and a gamut of application domains. Consequently, researchers have sought measures of association that are invariant under strictly increasing transformations of the margins, with the extant literature being splintered. Rank correlation coefficients, such as Spearman's Rho and Kendall's Tau, have been studied at great length in the statistical literature, mostly under the assumption that $X$ and $Y$ are continuous. In the case of a dichotomous outcome $Y$, receiver operating characteristic (ROC) analysis and the asymmetric area under the ROC curve (AUC) measure are used to assess monotone dependence of $Y$ on a covariate $X$. In this talk I demonstrate that the two thus far disconnected strands of literature can be unified and bridged, by developing common population level theory, common estimators, and common tests that apply to all types of linearly ordered outcomes. In case studies, we assess progress in artificial intelligence (AI) based weather prediction and evaluate methods of uncertainty quantification for the output of large language models. The talk is based on joint work with Eva-Maria Walz and Andreas Eberl.

Assessing Monotone Dependenceread_more

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