Past Events

  • 2017 Feb 07

    STAT 300: Reagan Rose and Jameson Quinn

    12:20pm to 1:10pm

    Location: 

    Science Center Rm. 705

    "Guiding jurors with prior-award information: a case study in causal inference from factorial design" by Reagan Rose

    "Using locality to beat the curse of dimension in particle filters" by Jameson Quinn

  • 2017 Feb 06

    Colloq: Rob Tibshirani

    4:15pm to 5:15pm

    Location: 

    Science Center Hall D

    Recent Advances in Post-Selection Statistical Inference

    We describe the problem of “post-selection inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the...

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  • 2017 Feb 01

    Seminar: Lucas Janson

    12:00pm to 1:00pm

    Location: 

    Science Center Rm. 705

    Model-free knockoffs: high-dimensional variable selection that controls the false discovery rate

    Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a nonlinear fashion, such as when the response is binary. Although this modeling problem has been extensively studied, it remains unclear how to effectively control the fraction of false discoveries even in high-dimensional logistic regression, not to mention general high-dimensional nonlinear models. To address such a practical problem, we propose...

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  • 2017 Jan 31

    Seminar: Rajarshi Mukherjee

    12:00pm to 1:00pm

    Location: 

    Science Center Rm. 705

    Sparse Signal Detection with Binary Outcomes

    In this talk, I will discuss three examples of sparse signal detection problems in the context of binary outcomes. These will be motivated by examples from next generation sequencing association studies, understanding heterogeneities in large scale networks, and exploring opinion distributions over networks. Moreover, these three examples will serve as templates to explore interesting phase transitions present in such studies. In particular, these phase transitions will be aimed at revealing a difference between studies with possibly...

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  • 2017 Jan 30

    Colloq: Alexander (Sasha) Rakhlin

    4:15pm to 5:15pm

    Location: 

    Science Center Hall E

    An Optimal Aggregation Procedure For Nonparametric Regression

    How can one combine a collection of estimators of a regression function into a good aggregate? In the last 15 years, this age-old question has received increasing attention within the Mathematical Statistics community. A closely related question of regression in misspecified models has been studied within Statistical Learning using the techniques of empirical processes. We outline the shortcomings of...

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  • 2017 Jan 26

    Seminar: Edgar Dobriban

    12:00pm to 1:00pm

    Location: 

    Science Center Rm. 705

    ePCA: Exponential family PCA

    Many applications, such as photon-limited imaging and genomics, involve large datasets with entries from exponential family distributions. It is of interest to estimate the covariance structure and principal components of the noiseless distribution. Principal Component Analysis (PCA), the standard method for this setting, can be inefficient for non-Gaussian noise. In this talk we present ePCA, a methodology for PCA on exponential family distributions. ePCA involves the eigendecomposition of a new covariance matrix estimator, constructed in a...

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