Search Results for author: Chiara Sabatti

Found 4 papers, 3 papers with code

With Malice Towards None: Assessing Uncertainty via Equalized Coverage

1 code implementation15 Aug 2019 Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, Emmanuel J. Candès

An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers.

Prediction Intervals Recommendation Systems

Controlling FDR while highlighting selected discoveries

1 code implementation6 Sep 2018 Eugene Katsevich, Chiara Sabatti, Marina Bogomolov

Often modern scientific investigations start by testing a very large number of hypotheses in an effort to comprehensively mine the data for possible discoveries.


Detecting Multiple Replicating Signals using Adaptive Filtering Procedures

1 code implementation11 Oct 2016 Jingshu Wang, Lin Gui, Weijie J. Su, Chiara Sabatti, Art B. Owen

Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, study populations, across time etc.


SLOPE - Adaptive variable selection via convex optimization

no code implementations14 Jul 2014 Małgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su, Emmanuel J. Candès

SLOPE, short for Sorted L-One Penalized Estimation, is the solution to \[\min_{b\in\mathbb{R}^p}\frac{1}{2}\Vert y-Xb\Vert _{\ell_2}^2+\lambda_1\vert b\vert _{(1)}+\lambda_2\vert b\vert_{(2)}+\cdots+\lambda_p\vert b\vert_{(p)},\] where $\lambda_1\ge\lambda_2\ge\cdots\ge\lambda_p\ge0$ and $\vert b\vert_{(1)}\ge\vert b\vert_{(2)}\ge\cdots\ge\vert b\vert_{(p)}$ are the decreasing absolute values of the entries of $b$.


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