142 citations · 338 across the 19 of their papers we have counts for
16 papers · 1 filter
Statistical Query Lower Bounds for List-Decodable Linear Regression
Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia +2
We study the problem of list-decodable linear regression, where an adversary can corrupt a majority of the examples. Specifically, we are given a set of labeled examples $(x, y…
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Ilias Diakonikolas, Sushrut Karmalkar, Daniel Kane +2
We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental…
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
Brian Axelrod, Ilias Diakonikolas, Anastasios Sidiropoulos +2
We consider the problem of computing the maximum likelihood multivariate log-concave distribution for a set of points. Specifically, we present an algorithm which, given points…
A Polynomial Time Algorithm for Maximum Likelihood Estimation of Multivariate Log-concave Densities
Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart
We study the problem of computing the maximum likelihood estimator (MLE) of multivariate log-concave densities. Our main result is the first computationally efficient algorithm for…
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…
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…
Testing Bayesian Networks
Clement Canonne, Ilias Diakonikolas, Daniel Kane +1
This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks -- the prototypical family of directed…
Efficient Robust Proper Learning of Log-concave Distributions
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We study the {\em robust proper learning} of univariate log-concave distributions (over continuous and discrete domains). Given a set of samples drawn from an unknown target distri…
Robust Learning of Fixed-Structure Bayesian Networks
Yu Cheng, Ilias Diakonikolas, Daniel Kane +1
We investigate the problem of learning Bayesian networks in a robust model where an -fraction of the samples are adversarially corrupted. In this work, we study the fully observ…
Robust Estimators in High Dimensions without the Computational Intractability
Ilias Diakonikolas, Gautam Kamath, Daniel Kane +3
We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an -fraction of the samples. Such questions…
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We give an algorithm for properly learning Poisson binomial distributions. A Poisson binomial distribution (PBD) of order is the discrete probability distribution of the sum of…
The Fourier Transform of Poisson Multinomial Distributions and its Algorithmic Applications
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
An -Poisson Multinomial Distribution (PMD) is a random variable of the form , where the 's are independent random vectors supported on the set of…
Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
We study the structure and learnability of sums of independent integer random variables (SIIRVs). For , a -SIIRV of order is the pro…