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- Victor-Emmanuel Brunel
- NeurIPS
- 2018

. Symmetric determinantal point processes (DPP’s) are a class of probabilistic models that encode the random selection of items that exhibit a repulsive behavior. They have attracted a lot of… (More)

- Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek
- ArXiv
- 2018

This paper studies active learning in the context of robust statistics. Specifically, we propose a variant of the Best Arm Identification problem for contaminated bandits, where each arm pull has… (More)

Tukey’s halfaspace depth has attracted much interest in data analysis, because it is a natural way of measuring the notion of depth relative to a cloud of points or, more generally, to a probability… (More)

Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been… (More)

We estimate the support of a uniform density, when it is assumed to be a convex polytope or, more generally, a convex body in R. In the polytopal case, we construct an estimator achieving a rate… (More)

We study the Hausdorff distance between a random polytope, defined as the convex hull of i.i.d. random points, and the convex hull of the support of their distribution. As particular examples, we… (More)

Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been… (More)

Determinantal Point Processes (DPPs) are a family of probabilistic models that have a repulsive behavior, and lend themselves naturally to many tasks in machine learning where returning a diverse set… (More)

We estimate the support of a uniform density, when it is assumed to be a convex polytope or, more generally, a convex body in $\R^d$. In the polytopal case, we construct an estimator achieving a rate… (More)