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          <dc:title>The Large-Error Approximate Degree of AC^0</dc:title>
          <dc:creator>Bun, Mark</dc:creator>
          <dc:creator>Thaler, Justin</dc:creator>
          <dc:subject>approximate degree</dc:subject>
          <dc:subject>discrepancy</dc:subject>
          <dc:subject>margin complexity</dc:subject>
          <dc:subject>polynomial approximations</dc:subject>
          <dc:subject>secret sharing</dc:subject>
          <dc:subject>threshold circuits</dc:subject>
          <dc:description>We prove two new results about the inability of low-degree polynomials to uniformly approximate constant-depth circuits, even to slightly-better-than-trivial error. First, we prove a tight Omega~(n^{1/2}) lower bound on the threshold degree of the SURJECTIVITY function on n variables. This matches the best known threshold degree bound for any AC^0 function, previously exhibited by a much more complicated circuit of larger depth (Sherstov, FOCS 2015). Our result also extends to a 2^{Omega~(n^{1/2})} lower bound on the sign-rank of an AC^0 function, improving on the previous best bound of 2^{Omega(n^{2/5})} (Bun and Thaler, ICALP 2016). &#13;
Second, for any delta&gt;0, we exhibit a function f : {-1,1}^n -&gt; {-1,1} that is computed by a circuit of depth O(1/delta) and is hard to approximate by polynomials in the following sense: f cannot be uniformly approximated to error epsilon=1-2^{-Omega(n^{1-delta})}, even by polynomials of degree n^{1-delta}. Our recent prior work (Bun and Thaler, FOCS 2017) proved a similar lower bound, but which held only for error epsilon=1/3.&#13;
Our result implies 2^{Omega(n^{1-delta})} lower bounds on the complexity of AC^0 under a variety of basic measures such as discrepancy, margin complexity, and threshold weight. This nearly matches the trivial upper bound of 2^{O(n)} that holds for every function. The previous best lower bound on AC^0 for these measures was 2^{Omega(n^{1/2})} (Sherstov, FOCS 2015). Additional applications in learning theory, communication complexity, and cryptography are described.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Mark Bun and Justin Thaler</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>
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          <dc:identifier>doi:10.4230/LIPIcs.APPROX-RANDOM.2019.55</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-112709</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2019.55</dc:identifier>
          <dc:language>eng</dc:language>
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