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DTSTART;TZID=America/Los_Angeles:20261026T161500
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DTSTAMP:20261016T164717Z
CREATED:20261016T163415Z
LAST-MODIFIED:20261016T164717Z
UID:10002369-1793031300-1793034000@colleges.claremont.edu
SUMMARY:Initial Conditions and the Big Push (Daniel Velasquez\, CMC)
DESCRIPTION:This paper examines how initial conditions shape the long-run impact of large temporary shocks on economic geography. Using WWII supply contracts in the United States\, we show that temporary spending had persistent effects on population and manufacturing employment only in counties with low initial manufacturing development. Motivated by these findings\, we develop a multisector economic geography model that features external economies of scale in manufacturing with non-constant elasticity. We calibrate the model and show that we match the empirical estimates with an S-shaped scale function for which scale economies large enough to cause coordination failures are limited to locations with low initial manufacturing employment. We use the calibrated model to re-evaluate how temporary WWII spending shaped the evolution of U.S. postwar economic geography. Despite the presence of big push dynamics and multiple steady states\, the aggregate effects of WWII spending on long-term outcomes are limited due to the restricted domain over which coordination failures operate. \nYou can find an online version of the paper here:  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5408984
URL:https://colleges.claremont.edu/ccms/event/initial-conditions-and-the-big-push-daniel-velasquez-cmc/
LOCATION:Estella 1021 (Emmy Noether Room)\, Pomona College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ami Radunskaya":MAILTO:aradunskaya@pomona.edu
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DTSTART;TZID=America/Los_Angeles:20261102T161500
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DTSTAMP:20261007T205549Z
CREATED:20261007T204617Z
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UID:10002365-1793636100-1793639700@colleges.claremont.edu
SUMMARY:In-Context Kernel Ridge Regression with Transformers (Mingsong Yan\, UCSB)
DESCRIPTION:Large language models (LLMs) can use examples provided in a prompt to make predictions for new queries without updating their parameters\, a capability known as in-context learning. In this talk\, I will explore how transformers\, the architectural backbone of many LLMs\, can perform nonlinear kernel ridge regression (KRR). I will first present numerical observations showing that transformers pretrained on regression tasks match KRR predictions more closely as depth increases. Motivated by these observations\, I will present a theoretical construction showing how a transformer can approximate the Gaussian KRR predictor through an intuitive connection to a convergent iterative solver. This construction has a natural interpretation: successive transformer layers approximate steps of the solver\, progressively refining the solution to the underlying kernel system. Together\, these results provide theoretical and empirical insights into in-context learning with transformers for nonlinear regression.
URL:https://colleges.claremont.edu/ccms/event/in-context-kernel-ridge-regression-with-transformers/
LOCATION:Estella 1021 (Emmy Noether Room)\, Pomona College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ami Radunskaya":MAILTO:aradunskaya@pomona.edu
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