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20162026
most citedClustering with Noisy Queries

37 citations · 67 across the 18 of their papers we have counts for

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Showing 2021 · cs.DSShow all

6 papers · 2 filters

cs.DS2021

Improved Approximation Algorithms for Dyck Edit Distance and RNA Folding

Debarati Das, Tomasz Kociumaka, Barna Saha

The Dyck language, which consists of well-balanced sequences of parentheses, is one of the most fundamental context-free languages. The Dyck edit distance quantifies the number of…

cs.DS2021

How Compression and Approximation Affect Efficiency in String Distance Measures

Arun Ganesh, Tomasz Kociumaka, Andrea Lincoln +1

Real-world data often comes in compressed form. Analyzing compressed data directly (without decompressing it) can save space and time by orders of magnitude. In this work, we focus…

cs.DS2021

Gap Edit Distance via Non-Adaptive Queries: Simple and Optimal

Elazar Goldenberg, Tomasz Kociumaka, Robert Krauthgamer +1

We study the problem of approximating edit distance in sublinear time. This is formalized as the -Gap Edit Distance problem, where the input is a pair of strings and…

cs.DS2021

Approximating LCS and Alignment Distance over Multiple Sequences

Debarati Das, Barna Saha

We study the problem of aligning multiple sequences with the goal of finding an alignment that either maximizes the number of aligned symbols (the longest common subsequence (LCS))…

cs.DS2021

Does Preprocessing help in Fast Sequence Comparisons?

Elazar Goldenberg, Aviad Rubinstein, Barna Saha

We study edit distance computation with preprocessing: the preprocessing algorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed…

cs.DS2021

How to Design Robust Algorithms using Noisy Comparison Oracle

Raghavendra Addanki, Sainyam Galhotra, Barna Saha

Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as -center clustering an…