Dear Workshop Community,
Our next meeting will be *Wednesday November 20*, where Lucas Janson will
present research on* "*Recent Advances in Model-X Knockoffs*"*
*Abstract*: Two years ago in this workshop I presented my work on model-X
knockoffs, a method for high-dimensional variable selection that provides
exact (finite-sample) control of false discoveries and high power as a
result of its flexibility to leverage any and all domain knowledge and
tools from machine learning to search for signal. In this talk, I will
discuss two recent works that significantly advance the usability and
generality of model-X knockoffs. First, I will show how the original
assumptions of model-X knockoffs, that the multivariate distribution of the
covariates be known exactly, can be significantly relaxed to the assumption
that only a *model* for the covariates be known, and that model can have as
many free parameters as the *product* of the sample size and dimension. No
loss in the guarantees of knockoffs is incurred by this relaxation of the
assumptions. Second, I will show how to efficiently and exactly sample
knockoffs for *any *distribution on the covariates, even if the
distribution is only known up to a normalization constant. This
dramatically expands the set of covariate distribution for which we can
apply knockoffs. This is joint work with a number of collaborators, listed
below in the full references for the two works:
*D. Huang and L. Janson. Relaxing the Assumptions of Knockoffs by
Conditioning. Annals of Statistics (to appear), 2019.*
*S. Bates, E. Candès, L. Janson, and W. Wang. Metropolized Knockoff
Sampling. 2019.*
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, November 20 at 12 noon - 1:30 pm.
All are welcome! Lunch will be provided.
Best,
Georgie
Show replies by date