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DTSTART:20070311T020000
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BEGIN:VEVENT
DTSTAMP:20240829T142112
DTSTART;TZID=America/Detroit:20241126T140000
DTEND;TZID=America/Detroit:20241126T150000
SUMMARY:Recreational / Games:Schokoladenstunde
DESCRIPTION:German Lecturer\, Silvia Grzeskowiak (sgrzesko@umich.edu)\, brings German chocolate to snack on and games to play (e.g. Tabu)\, all while chatting in German.
UID:125319-21854711@events.umich.edu
URL:https://events.umich.edu/event/125319
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Games,German,German Studies,Germanic Languages And Literatures
LOCATION:Modern Languages Building - 3110
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240830T110328
DTSTART;TZID=America/Detroit:20241126T143000
DTEND;TZID=America/Detroit:20241126T153000
SUMMARY:Social / Informal Gathering:French Conversation Hour: *Pause - Café*
DESCRIPTION:Enjoy coffee\, tea\, and snacks while improving your French skills!\nChat for 10 minutes or the whole hour! All language levels welcome.
UID:125405-21854918@events.umich.edu
URL:https://events.umich.edu/event/125405
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Europe,Undergraduate,Social,Romance Languages And Literatures,multicultural,Language,International,intercultural,In Person,Global,French,Free,Discussion,Culture,Community-based Learning,Community,Coffee
LOCATION:Modern Languages Building - RLL Commons (MLB 4314)
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20241114T140149
DTSTART;TZID=America/Detroit:20241126T160000
DTEND;TZID=America/Detroit:20241126T170000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Shuting Shen\, Postdoctoral Research Fellow\, Fuqua School of Business and the Department of Biostatistics & Bioinformatics\, Duke University.
DESCRIPTION:Abstract: The modern retailing system is witnessing fast updating in product features and customer behaviors\, entailing adaptive policies that can effectively capture the dynamics of customer preferences. To optimize potential revenues and manage the risks associated with changing\ncustomer preferences\, it is important to develop an online framework that quantifies the uncertainty of the optimal assortment adaptively. \n\nWe study the combinatorial inference of the optimal assortment within the framework of the contextual multinomial logit model. In this setting\, customer choice outcomes are actively collected over a series of time points\, where the contextual information for products—including embedding vectors that capture the customer-product dynamics\, as well as revenue parameters—varies over time. Using a dynamic policy\, the offer set is adaptively selected at each time point based on historical data. We propose an inferential procedure that constructs a discrete confidence set for the true optimal assortment based upon the data collected by the dynamic policy\, which can be applied to test any combinatorial properties of the optimal assortment\, such as the number of product categories to include in the offer set.\n\nThe temporal dependency and combinatorial data structure due to adaptive sampling create challenges for convergence analysis. To address these\, we develop new probabilistic results on anti-concentration bounds for the difference between the maxima of two Gaussian random vectors. Furthermore\, we address the high dimensionality of the combinatorial inference problem by employing discretization via epsilon-covering and subspace projection techniques. We provide theoretical guarantees on both the validity and power of our inferential procedure\, and establish information-theoretic lower bounds for the required signal strength\, which match the upper bounds of our procedure up to logarithmic factors.\n\nhttps://judygiant.github.io/
UID:124598-21853250@events.umich.edu
URL:https://events.umich.edu/event/124598
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 411
CONTACT:
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