142 citations · 338 across the 19 of their papers we have counts for
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Outlier-Robust Learning of Ising Models Under Dobrushin's Condition
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart +1
We study the problem of learning Ising models satisfying Dobrushin's condition in the outlier-robust setting where a constant fraction of the samples are adversarially corrupted. O…
Efficient Algorithms and Lower Bounds for Robust Linear Regression
Ilias Diakonikolas, Weihao Kong, Alistair Stewart
We study the problem of high-dimensional linear regression in a robust model where an -fraction of the samples can be adversarially corrupted. We focus on the fundamental settin…
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
In high dimensions, most machine learning methods are brittle to even a small fraction of structured outliers. To address this, we introduce a new meta-algorithm that can take in a…
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…
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…
Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We describe a general technique that yields the first {\em Statistical Query lower bounds} for a range of fundamental high-dimensional learning problems involving Gaussian distribu…
Learning Multivariate Log-concave Distributions
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We study the problem of estimating multivariate log-concave probability density functions. We prove the first sample complexity upper bound for learning log-concave densities on $\…