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DTSTAMP:20241008T092531
DTSTART;TZID=America/Detroit:20241011T143000
DTEND;TZID=America/Detroit:20241011T155000
SUMMARY:Workshop / Seminar:Principled Mechanism Design with Evidence
DESCRIPTION:We cast mechanism design with evidence in the framework of Myerson (1982)\, whereby his generalized revelation principle directly applies and yields standard no- tions of incentive compatible direct mechanisms. Their specific nature depends on whether the agent’s (verifiable) presentation of evidence is contractually controllable\, however. For deterministic implementation\, we show that\, in general\, such control has value\, and we offer two independent conditions under which this value vanishes\, one on evidence (WET) and another on preferences (TIWO). Allowing for fully stochastic mechanisms\, we also show how randomization generally has value and clarify to what extent this value vanishes under the common assumption of evidentiary normality (NOR). While\, in general\, the value of control extends to stochastic implementation\, neither control nor randomization have any value if NOR holds together with WET or TIWO.
UID:127513-21859250@events.umich.edu
URL:https://events.umich.edu/event/127513
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Economics,Microeconomics,seminar,Theory
LOCATION:Lorch Hall - 301
CONTACT:
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DTSTAMP:20240920T225457
DTSTART;TZID=America/Detroit:20241011T150000
DTEND;TZID=America/Detroit:20241011T160000
SUMMARY:Lecture / Discussion:AIM Seminar: A deterministic PDE perspective on diffusion models
DESCRIPTION:Abstract:  Generative modeling represents one of the most striking examples of the successes of modern machine learning.  In generative modeling\, one seeks to artificially generate new data that belongs to given input class.  For instance\, generating new faces from a database of celebrity faces.   Mathematically\, this can be posed as finding a map that pushes a simple concrete probability distribution\, such as a standard Gaussian\, to a complicated abstract distribution that is only known through examples.   \n\nCurrently\, one of the most popular paradigms in generative modeling is diffusion modeling\, where the map is constructed by reversing the flow of a diffusion equation applied to the example set.  The standard approach in the literature focuses on flowing along a Fokker-Planck equation (i.e. a heat equation with a drift term) and requires stochasticity in the backwards flow to prove convergence rates.  In this talk\, I will discuss some advantages of flowing along a more general class of diffusion equations and prove convergence rates when the backwards flow is deterministic. \n\nContact:  Selim Esedoglu.
UID:121469-21846581@events.umich.edu
URL:https://events.umich.edu/event/121469
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1084
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240926T220017
DTSTART;TZID=America/Detroit:20241011T150000
DTEND;TZID=America/Detroit:20241011T160000
SUMMARY:Workshop / Seminar:Double orthodontic polynomials (Combinatorics seminar)
DESCRIPTION:Motivated by our search for a representation-theoretic avatar of double Grothendieck polynomials G_w(x\;y)\, we give a new formula for G_w(x\;y) based on Magyar's orthodontia algorithm for diagrams. We obtain a similar formula for double Schubert polynomials S_w(x\;y)\, and a curious positivity result: For vexillary permutations w\, the polynomial x_1^n \dots x_n^n S_w(x_n^{-1}\, \dots\, x_1^{-1}\; 1\, \dots\, 1) is a graded nonnegative sum of Lascoux polynomials. This is joint work with Avery St. Dizier.
UID:124468-21853063@events.umich.edu
URL:https://events.umich.edu/event/124468
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 4096
CONTACT:
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