BibTeX Export for Agnostic Learning from Tolerant Natural Proofs

Copy to Clipboard Download

@InProceedings{carmosino_et_al:LIPIcs.APPROX-RANDOM.2017.35,
  author =	{Carmosino, Marco L. and Impagliazzo, Russell and Kabanets, Valentine and Kolokolova, Antonina},
  title =	{{Agnostic Learning from Tolerant Natural Proofs}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017)},
  pages =	{35:1--35:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-044-6},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{81},
  editor =	{Jansen, Klaus and Rolim, Jos\'{e} D. P. and Williamson, David P. and Vempala, Santosh S.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2017.35},
  URN =		{urn:nbn:de:0030-drops-75842},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2017.35},
  annote =	{Keywords: agnostic learning, natural proofs, circuit lower bounds, meta-algorithms, AC0\lbrackq\rbrack, Nisan-Wigderson generator}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail