@InProceedings{hu_et_al:LIPIcs.ITCS.2023.72,
  author =	{Hu, Lunjia and Peale, Charlotte},
  title =	{{Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes}},
  booktitle =	{14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
  pages =	{72:1--72:30},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-263-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{251},
  editor =	{Tauman Kalai, Yael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.72},
  URN =		{urn:nbn:de:0030-drops-175752},
  doi =		{10.4230/LIPIcs.ITCS.2023.72},
  annote =	{Keywords: Comparative learning, mutual VC dimension, realizable multiaccuracy and multicalibration, sample complexity}
}