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Electronic Colloquium on Computational Complexity

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All reports by Author Grigory Yaroslavtsev:

TR18-169 | 18th September 2018
Kaave Hosseini, Shachar Lovett, Grigory Yaroslavtsev

Optimality of Linear Sketching under Modular Updates

We study the relation between streaming algorithms and linear sketching algorithms, in the context of binary updates. We show that for inputs in $n$ dimensions,
the existence of efficient streaming algorithms which can process $\Omega(n^2)$ updates implies efficient linear sketching algorithms with comparable cost.
This improves upon the previous work ... more >>>

TR16-174 | 7th November 2016
Elchanan Mossel, Sampath Sampath Kannan, Grigory Yaroslavtsev

Linear Sketching over $\mathbb F_2$

Revisions: 5 , Comments: 2

We initiate a systematic study of linear sketching over $\mathbb F_2$. For a given Boolean function $f \colon \{0,1\}^n \to \{0,1\}$ a randomized $\mathbb F_2$-sketch is a distribution $\mathcal M$ over $d \times n$ matrices with elements over $\mathbb F_2$ such that $\mathcal Mx$ suffices for computing $f(x)$ with high ... more >>>

TR15-031 | 2nd March 2015
Marco Molinaro, David Woodruff, Grigory Yaroslavtsev

Amplification of One-Way Information Complexity via Codes and Noise Sensitivity

Revisions: 1

We show a new connection between the information complexity of one-way communication problems under product distributions and a relaxed notion of list-decodable codes. As a consequence, we obtain a characterization of the information complexity of one-way problems under product distributions for any error rate based on covering numbers. This generalizes ... more >>>

TR13-036 | 13th March 2013
Eric Blais, Sofya Raskhodnikova, Grigory Yaroslavtsev

Lower Bounds for Testing Properties of Functions on Hypergrid Domains

Revisions: 1

We introduce strong, and in many cases optimal, lower bounds for the number of queries required to nonadaptively test three fundamental properties of functions $ f : [n]^d \rightarrow \mathbb R$ on the hypergrid: monotonicity, convexity, and the Lipschitz property.
Our lower bounds also apply to the more restricted setting ... more >>>

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