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        <datestamp>2024-03-06T10:47:52Z</datestamp>
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          <dc:title>Pairwise Independent Random Walks Can Be Slightly Unbounded</dc:title>
          <dc:creator>Narayanan, Shyam</dc:creator>
          <dc:subject>k-wise Independence</dc:subject>
          <dc:subject>Random Walks</dc:subject>
          <dc:subject>Moments</dc:subject>
          <dc:subject>Chaining</dc:subject>
          <dc:description>A family of problems that have been studied in the context of various streaming algorithms are generalizations of the fact that the expected maximum distance of a 4-wise independent random walk on a line over n steps is O(sqrt{n}). For small values of k, there exist k-wise independent random walks that can be stored in much less space than storing n random bits, so these properties are often useful for lowering space bounds. In this paper, we show that for all of these examples, 4-wise independence is required by demonstrating a pairwise independent random walk with steps uniform in +/- 1 and expected maximum distance Omega(sqrt{n} lg n) from the origin. We also show that this bound is tight for the first and second moment, i.e. the expected maximum square distance of a 2-wise independent random walk is always O(n lg^2 n). Also, for any even k &gt;= 4, we show that the kth moment of the maximum distance of any k-wise independent random walk is O(n^{k/2}). The previous two results generalize to random walks tracking insertion-only streams, and provide higher moment bounds than currently known. We also prove a generalization of Kolmogorov’s maximal inequality by showing an asymptotically equivalent statement that requires only 4-wise independent random variables with bounded second moments, which also generalizes a result of Błasiok.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Shyam Narayanan</dc:contributor>
          <dc:date>2019</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 145, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.APPROX-RANDOM.2019.63</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-112787</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2019.63</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
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