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REPORTS > AUTHORS > ILIAS DIAKONIKOLAS:
All reports by Author Ilias Diakonikolas:

TR17-133 | 7th September 2017
Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price

Sample-Optimal Identity Testing with High Probability

We study the problem of testing identity against a given distribution (a.k.a. goodness-of-fit) with a focus on the high confidence regime. More precisely, given samples from an unknown distribution $p$ over $n$ elements, an explicitly given distribution $q$, and parameters $0< \epsilon, \delta < 1$, we wish to distinguish, {\em ... more >>>


TR17-132 | 7th September 2017
Ilias Diakonikolas, Daniel Kane, Alistair Stewart

Sharp Bounds for Generalized Uniformity Testing

We study the problem of {\em generalized uniformity testing}~\cite{BC17} of a discrete probability distribution: Given samples from a probability distribution $p$ over an {\em unknown} discrete domain $\mathbf{\Omega}$, we want to distinguish, with probability at least $2/3$, between the case that $p$ is uniform on some {\em subset} of $\mathbf{\Omega}$ ... more >>>


TR17-075 | 29th April 2017
Clement Canonne, Ilias Diakonikolas, Alistair Stewart

Fourier-Based Testing for Families of Distributions

Revisions: 1

We study the general problem of testing whether an unknown discrete distribution belongs to a given family of distributions. More specifically, given a class of distributions $\mathcal{P}$ and sample access to an unknown distribution $\mathbf{P}$, we want to distinguish (with high probability) between the case that $\mathbf{P} \in \mathcal{P}$ and ... more >>>


TR16-178 | 11th November 2016
Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price

Collision-based Testers are Optimal for Uniformity and Closeness

Comments: 1

We study the fundamental problems of (i) uniformity testing of a discrete distribution,
and (ii) closeness testing between two discrete distributions with bounded $\ell_2$-norm.
These problems have been extensively studied in distribution testing
and sample-optimal estimators are known for them~\cite{Paninski:08, CDVV14, VV14, DKN:15}.

In this work, we show ... more >>>


TR16-177 | 11th November 2016
Ilias Diakonikolas, Daniel Kane, Alistair Stewart

Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures

Revisions: 1

We prove the first {\em Statistical Query lower bounds} for two fundamental high-dimensional learning problems involving Gaussian distributions: (1) learning Gaussian mixture models (GMMs), and (2) robust (agnostic) learning of a single unknown mean Gaussian. In particular, we show a {\em super-polynomial gap} between the (information-theoretic) sample complexity and the ... more >>>


TR16-074 | 9th May 2016
Ilias Diakonikolas, Daniel Kane

A New Approach for Testing Properties of Discrete Distributions

We study problems in distribution property testing:
Given sample access to one or more unknown discrete distributions,
we want to determine whether they have some global property or are $\epsilon$-far
from having the property in $\ell_1$ distance (equivalently, total variation distance, or ``statistical distance'').
In this work, we give a ... more >>>


TR13-172 | 1st December 2013
Anindya De, Ilias Diakonikolas, Rocco Servedio

Deterministic Approximate Counting for Degree-$2$ Polynomial Threshold Functions

We give a {\em deterministic} algorithm for approximately computing the fraction of Boolean assignments that satisfy a degree-$2$ polynomial threshold function. Given a degree-2 input polynomial $p(x_1,\dots,x_n)$ and a parameter $\eps > 0$, the algorithm approximates
\[
\Pr_{x \sim \{-1,1\}^n}[p(x) \geq 0]
\]
to within an additive $\pm \eps$ in ... more >>>


TR13-171 | 1st December 2013
Anindya De, Ilias Diakonikolas, Rocco Servedio

Deterministic Approximate Counting for Juntas of Degree-$2$ Polynomial Threshold Functions

Let $g: \{-1,1\}^k \to \{-1,1\}$ be any Boolean function and $q_1,\dots,q_k$ be any degree-2 polynomials over $\{-1,1\}^n.$ We give a \emph{deterministic} algorithm which, given as input explicit descriptions of $g,q_1,\dots,q_k$ and an accuracy parameter $\eps>0$, approximates \[
\Pr_{x \sim \{-1,1\}^n}[g(\sign(q_1(x)),\dots,\sign(q_k(x)))=1] \]
to within an additive $\pm \eps$. For any constant ... more >>>


TR12-181 | 20th December 2012
Anindya De, Ilias Diakonikolas, Rocco Servedio

The Inverse Shapley Value Problem

For $f$ a weighted voting scheme used by $n$ voters to choose between two candidates, the $n$ \emph{Shapley-Shubik Indices} (or {\em Shapley values}) of $f$ provide a measure of how much control each voter can exert over the overall outcome of the vote. Shapley-Shubik indices were introduced by Lloyd Shapley ... more >>>


TR12-152 | 7th November 2012
Anindya De, Ilias Diakonikolas, Rocco Servedio

Inverse Problems in Approximate Uniform Generation

We initiate the study of \emph{inverse} problems in approximate uniform generation, focusing on uniform generation of satisfying assignments of various types of Boolean functions. In such an inverse problem, the algorithm is given uniform random satisfying assignments of an unknown function $f$ belonging to a class $\C$ of Boolean functions ... more >>>


TR12-072 | 5th June 2012
Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco Servedio

Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces

The \emph{Chow parameters} of a Boolean function $f: \{-1,1\}^n \to \{-1,1\}$ are its $n+1$ degree-0 and degree-1 Fourier coefficients. It has been known since 1961 \cite{Chow:61, Tannenbaum:61} that the (exact values of the) Chow parameters of any linear threshold function $f$ uniquely specify $f$ within the space of all Boolean ... more >>>




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