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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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DTSTAMP:20241124T172309
DTSTART;TZID=America/Detroit:20241126T170000
DTEND;TZID=America/Detroit:20241126T180000
SUMMARY:Workshop / Seminar:Dyadic Grids and Sparse Domination
DESCRIPTION:In harmonic analysis\, maximal operators are a helpful tool to allow us to prove convergence results for nicer dense subspaces. A famous and reasonably simple example is the maximal function operator\, used to prove the famous Lebesgue Differentiation Theorem\, and likely to appear in any analysis class. In this talk\, we will introduce one of its many cousins - the dyadic maximal function - which behaves very similarly but allows us to decompose our spaces into a \"good part\" and neatly organized \"bad parts\".\n\nThis is a technique inherent to the study of Calderòn-Zygmund operators (the Hilbert Transform cousins\, for those who have heard of this operator) and a crucial piece of the reasonably recent method of Sparse Domination. We hope to introduce a \"baby version\" of this technique to prove a weighted result for the dyadic maximal function\, an argument that can be adapted without much hurdle into a proof of the A2 conjecture for Calderòn-Zygmund operators - a conjecture on the dependence of long-established weighted bounds on the \"size\" of the weights - in a well-defined A2 characteristic sense.
UID:129421-21862707@events.umich.edu
URL:https://events.umich.edu/event/129421
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 4096
CONTACT:
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