142 citations · 338 across the 19 of their papers we have counts for
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List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We study the problem of list-decodable Gaussian mean estimation and the related problem of learning mixtures of separated spherical Gaussians. We develop a set of techniques that y…
Testing Conditional Independence of Discrete Distributions
Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane +1
We study the problem of testing \emph{conditional independence} for discrete distributions. Specifically, given samples from a discrete random variable on domain $[\ell…
Sharp Bounds for Generalized Uniformity Testing
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We study the problem of generalized uniformity testing \cite{BC17} of a discrete probability distribution: Given samples from a probability distribution over an {\em unknown} d…
Learning Geometric Concepts with Nasty Noise
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We study the efficient learnability of geometric concept classes - specifically, low-degree polynomial threshold functions (PTFs) and intersections of halfspaces - when a fraction…
Fourier-Based Testing for Families of Distributions
Clément L. Canonne, Ilias Diakonikolas, Alistair Stewart
We study the general problem of testing whether an unknown distribution belongs to a specified family of distributions. More specifically, given a distribution family …
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
We study the fundamental problem of learning the parameters of a high-dimensional Gaussian in the presence of noise -- where an -fraction of our samples were chosen by…
Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
Robust estimation is much more challenging in high dimensions than it is in one dimension: Most techniques either lead to intractable optimization problems or estimators that can t…