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Electronic Colloquium on Computational Complexity

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All reports by Author Elena Grigorescu:

TR17-088 | 10th May 2017
Elena Grigorescu, Akash Kumar, Karl Wimmer

K-Monotonicity is Not Testable on the Hypercube

We continue the study of $k$-monotone Boolean functions in the property testing model, initiated by Canonne et al. (ITCS 2017). A function $f:\{0,1\}^n\rightarrow \{0,1\}$ is said to be $k$-monotone if it alternates between $0$ and $1$ at most $k$ times on every ascending chain. Such functions represent a natural generalization ... more >>>

TR16-176 | 9th November 2016
Venkata Gandikota, Badih Ghazi, Elena Grigorescu

NP-Hardness of Reed-Solomon Decoding, and the Prouhet-Tarry-Escott Problem

Establishing the complexity of {\em Bounded Distance Decoding} for Reed-Solomon codes is a fundamental open problem in coding theory, explicitly asked by Guruswami and Vardy (IEEE Trans. Inf. Theory, 2005). The problem is motivated by the large current gap between the regime when it is NP-hard, and the regime when ... more >>>

TR16-136 | 31st August 2016
Clement Canonne, Elena Grigorescu, Siyao Guo, Akash Kumar, Karl Wimmer

Testing k-Monotonicity

Revisions: 1

A Boolean $k$-monotone function defined over a finite poset domain ${\cal D}$ alternates between the values $0$ and $1$ at most $k$ times on any ascending chain in ${\cal D}$. Therefore, $k$-monotone functions are natural generalizations of the classical monotone functions, which are the $1$-monotone functions.

Motivated by the ... more >>>

TR16-125 | 31st July 2016
Karthekeyan Chandrasekaran, Mahdi Cheraghchi, Venkata Gandikota, Elena Grigorescu

Local Testing for Membership in Lattices

Motivated by the structural analogies between point lattices and linear error-correcting codes, and by the mature theory on locally testable codes, we initiate a systematic study of local testing for membership in lattices. Testing membership in lattices is also motivated in practice, by applications to integer programming, error detection in ... more >>>

TR12-064 | 10th May 2012
Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh Vempala, Ying Xiao

Statistical Algorithms and a Lower Bound for Planted Clique

Revisions: 2

We develop a framework for proving lower bounds on computational problems over distributions, including optimization and unsupervised learning. Our framework is based on defining a restricted class of algorithms, called statistical algorithms, that instead of accessing samples from the input distribution can only obtain an estimate of the expectation ... more >>>

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