@InProceedings{schoenebeck_et_al:LIPIcs.ITCS.2021.78,
  author =	{Schoenebeck, Grant and Yu, Fang-Yi},
  title =	{{Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach}},
  booktitle =	{12th Innovations in Theoretical Computer Science Conference (ITCS 2021)},
  pages =	{78:1--78:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-177-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{185},
  editor =	{Lee, James R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2021.78},
  URN =		{urn:nbn:de:0030-drops-136177},
  doi =		{10.4230/LIPIcs.ITCS.2021.78},
  annote =	{Keywords: Information elicitation without verification, crowdsourcing, machine learning}
}