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Electronic Colloquium on Computational Complexity

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REPORTS > AUTHORS > ERIK WAINGARTEN:
All reports by Author Erik Waingarten:

TR19-165 | 18th November 2019
Clement Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten

Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning

We give a nearly-optimal algorithm for testing uniformity of distributions supported on $\{-1,1\}^n$, which makes $\tilde O (\sqrt{n}/\varepsilon^2)$ queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)). The key technical component is a natural notion of random restriction for distributions on $\{-1,1\}^n$, and a quantitative analysis of how ... more >>>


TR19-163 | 16th November 2019
Ramesh Krishnan S. Pallavoor, Sofya Raskhodnikova, Erik Waingarten

Approximating the Distance to Monotonicity of Boolean Functions

We design a nonadaptive algorithm that, given a Boolean function $f\colon \{0,1\}^n \to \{0,1\}$ which is $\alpha$-far from monotone, makes poly$(n, 1/\alpha)$ queries and returns an estimate that, with high probability, is an $\widetilde{O}(\sqrt{n})$-approximation to the distance of $f$ to monotonicity. Furthermore, we show that for any constant $\kappa > ... more >>>


TR19-134 | 4th October 2019
Omri Ben-Eliezer, Clement Canonne, Shoham Letzter, Erik Waingarten

Finding monotone patterns in sublinear time

We study the problem of finding monotone subsequences in an array from the viewpoint of sublinear algorithms. For fixed $k \in \mathbb{N}$ and $\varepsilon > 0$, we show that the non-adaptive query complexity of finding a length-$k$ monotone subsequence of $f \colon [n] \to \mathbb{R}$, assuming that $f$ is $\varepsilon$-far ... more >>>


TR18-094 | 2nd May 2018
Amit Levi, Erik Waingarten

Lower Bounds for Tolerant Junta and Unateness Testing via Rejection Sampling of Graphs

We introduce a new model for testing graph properties which we call the \emph{rejection sampling model}. We show that testing bipartiteness of $n$-nodes graphs using rejection sampling queries requires complexity $\widetilde{\Omega}(n^2)$. Via reductions from the rejection sampling model, we give three new lower bounds for tolerant testing of Boolean functions ... more >>>


TR17-068 | 20th April 2017
Xi Chen, Rocco Servedio, Li-Yang Tan, Erik Waingarten, Jinyu Xie

Settling the query complexity of non-adaptive junta testing

We prove that any non-adaptive algorithm that tests whether an unknown
Boolean function $f\colon \{0, 1\}^n\to\{0, 1\} $ is a $k$-junta or $\epsilon$-far from every $k$-junta must make $\widetilde{\Omega}(k^{3/2} / \epsilon)$ many queries for a wide range of parameters $k$ and $\epsilon$. Our result dramatically improves previous lower ... more >>>




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