72 citations · 399 across the 60 of their papers we have counts for
13 papers · 1 filter
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
Daniel Hsu, Clayton Sanford, Rocco Servedio +1
We consider the well-studied problem of learning intersections of halfspaces under the Gaussian distribution in the challenging \emph{agnostic learning} model. Recent work of Diako…
The perils of being unhinged: On the accuracy of classifiers minimizing a noise-robust convex loss
Philip M. Long, Rocco A. Servedio
Van Rooyen et al. introduced a notion of convex loss functions being robust to random classification noise, and established that the "unhinged" loss function is robust in this sens…
On the Approximation Power of Two-Layer Networks of Random ReLUs
Daniel Hsu, Clayton Sanford, Rocco A. Servedio +1
This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions? We give near-matching u…
Density estimation for shift-invariant multidimensional distributions
Anindya De, Philip M. Long, Rocco A. Servedio
We study density estimation for classes of shift-invariant distributions over . A multidimensional distribution is "shift-invariant" if, roughly speaking, it is close…
Learning sparse mixtures of rankings from noisy information
Anindya De, Ryan O'Donnell, Rocco Servedio
We study the problem of learning an unknown mixture of rankings over elements, given access to noisy samples drawn from the unknown mixture. We consider a range of differen…
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms
Siu-On Chan, Ilias Diakonikolas, Rocco A. Servedio +1
Let be an unknown and arbitrary probability distribution over . We consider the problem of {\em density estimation}, in which a learning algorithm is given i.i.d. draws…