Hi,
we are going to have Yves Grandvalet visiting on Monday 14th and Tuesday 15th, i.e. just after NIPS.
Yves is the head of the Heudiasyc lab (where Antoine Bordes and Nicolas Usunier worked before they joined Facebook), and a past collaborator of LISA (he spent a few months at LISA a couple of years ago). He's would also like to discuss possible collaboration avenues between our labs (maybe just after his talk).
------------------------------------------------------------------------------------ Title : Beyond Support in Two-Stage Variable Selection Who: Yves Grandvalet When: Tuesday, 15th, 11am Where: AA3195
Abstract: Numerous variable selection methods rely on a two-stage procedure, where a sparsity-inducing penalty is used in the first stage to predict the support, which is then conveyed to the second stage for estimation or inference purposes. In this framework, the first stage screens variables to find a set of possibly relevant variables and the second stage operates on this set of candidate variables, to improve estimation accuracy or to assess the uncertainty associated to the selection of variables.
We advocate that more information can be conveyed from the first stage to the second one: we use the magnitude of the coefficients estimated in the first stage to define an adaptive penalty that is applied at the second stage.
We give the example of an inference procedure that highly benefits from the proposed transfer of information. The procedure is precisely analyzed in a simple setting, and our large-scale experiments empirically demonstrate that actual benefits can be expected in much more general situations, with sensitivity gains ranging from 50% to 100% compared to state-of-the-art.
Keywords: Linear model, Lasso, Variable selection, p-values, False discovery rate, Screen and clean.
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Hi,
just a quick reminder:
Tuesday 15th at 11:00 we will have a presentation by Yves Grandvalet from Heudiasyc labs at UTC Compiègne near Paris.
Title : Beyond Support in Two-Stage Variable Selection Room: AA3195
Abstract: Numerous variable selection methods rely on a two-stage procedure, where a sparsity-inducing penalty is used in the first stage to predict the support, which is then conveyed to the second stage for estimation or inference purposes. In this framework, the first stage screens variables to find a set of possibly relevant variables and the second stage operates on this set of candidate variables, to improve estimation accuracy or to assess the uncertainty associated to the selection of variables.
We advocate that more information can be conveyed from the first stage to the second one: we use the magnitude of the coefficients estimated in the first stage to define an adaptive penalty that is applied at the second stage.
We give the example of an inference procedure that highly benefits from the proposed transfer of information. The procedure is precisely analyzed in a simple setting, and our large-scale experiments empirically demonstrate that actual benefits can be expected in much more general situations, with sensitivity gains ranging from 50% to 100% compared to state-of-the-art.
Keywords: Linear model, Lasso, Variable selection, p-values, False discovery rate, Screen and clean.