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<div>We are pleased to announce and invite you to the following talk in our ETH-FDS seminar series:</div>
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<div>„<b>On labels in supervised learning problems</b>“</div>
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<div>by <b>John Duchi, Stanford University</b></div>
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<div>Date and Time (Zurich): <span class="Apple-tab-span" style="white-space:pre">
</span>Thursday, 28 August 2025, 15.15 - 16.15</div>
<div>Place:<span class="Apple-tab-span" style="white-space:pre"> </span>Room to be announced (ETH Zentrum)</div>
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<div>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.</div>
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<div>Seminar websites: https://math.ethz.ch/sfs/news-and-events/data-science-seminar.html, https://math.ethz.ch/sfs/eth-foundations-of-data-science.html</div>
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<div>Organisers: A. Bandeira, H. Bölcskei, P. Bühlmann, J. Peters, A. Sousa, F. Yang</div>
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<div>ETH Zürich I Seminar für Statistik I Rämistrasse 101 I 8092 Zürich I Telefon +41 44 632 65 18 I sekretariat@stat.math.ethz.ch</div>
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