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        "event_title":"Principled Mechanism Design with Evidence",
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        "combined_title":"Principled Mechanism Design with Evidence: Roland Strausz, Humboldt University of Berlin",
        "event_subtitle":"Roland Strausz, Humboldt University of Berlin",
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        "event_type_id":"21",
        "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\u2019s (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.",
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        "tags":["Economics","Microeconomics","seminar","Theory"],
        "website":"http:\/\/www.lsa.umich.edu\/econ",
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        "event_title":"AIM Seminar: A deterministic PDE perspective on diffusion models",
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        "combined_title":"AIM Seminar: A deterministic PDE perspective on diffusion models: Matt Jacobs, University of California, Santa Barbara",
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        "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.   \r\n\r\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. \r\n\r\nContact:  Selim Esedoglu.",
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        "event_title":"Double orthodontic polynomials (Combinatorics seminar)",
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        "combined_title":"Double orthodontic polynomials (Combinatorics seminar): Linus Setiabrata, University of Chicago",
        "event_subtitle":"Linus Setiabrata, University of Chicago",
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        "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.",
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