BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Mário Figueiredo (Instituto Superior Técnico and IT) DTSTART:20210217T180000Z DTEND:20210217T190000Z DTSTAMP:20260423T003251Z UID:MPML/37 DESCRIPTION:Title: De aling with Correlated Variables in Supervised Learning\nby Mário Figu eiredo (Instituto Superior Técnico and IT) as part of Mathematics\, Physi cs and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nLinear (and generali zed linear) regression (LR) is an old\, but still essential\, statistical tool: its goal is to learn to predict a (response) variable from a linear combination of other (explanatory) variables. A central problem in LR is t he selection of relevant variables\, because using fewer variables tends t o yield better generalization and because this identification may be meani ngful (e.g.\, which genes are relevant to predict a certain disease). In t he past quarter-century\, variable selection (VS) based on sparsity-induci ng regularizers has been a central paradigm\, the most famous example bein g the LASSO\, which has been intensively studied\,\nextended\, and applied .\n\nIn many contexts\, it is natural to have highly-correlated variables (e.g.\, several genes that are strongly co-regulated)\, thus simultaneousl y relevant as predictors. In this case\, sparsity-based VS may fail: it ma y select an arbitrary subset of these variables and it is unstable. Moreov er\, it is often desirable to identify all the relevant variables\, not ju st an arbitrary subset thereof\, a goal for which several approaches have been proposed. This talk will be devoted to a recent class of such approac hes\, called ordered weighted l1 (OWL). The key feature of OWL is that it is provably able to explicitly identify (i.e. cluster) sufficiently-correl ated features\, without having to compute these correlations. Several theo retical results characterizing OWL will be presented\, including connectio ns to the mathematics of economic inequality. Computational and optimizati on aspects will also be addressed\, as well as recent applications in subs pace clustering\, learning Gaussian graphical models\, and deep neural net works.\n LOCATION:https://researchseminars.org/talk/MPML/37/ END:VEVENT END:VCALENDAR