مورد إلكتروني
Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
| العنوان: | Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach |
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| بيانات النشر: | LIPIcs - Leibniz International Proceedings in Informatics. 12th Innovations in Theoretical Computer Science Conference (ITCS 2021) 2021 |
| تفاصيل مُضافة: | Schoenebeck, Grant Yu, Fang-Yi |
| نوع الوثيقة: | Electronic Resource |
| مصطلحات الفهرس: | Information elicitation without verification, crowdsourcing, machine learning, Data processing Computer science, InProceedings, publishedVersion |
| DOI: | 10.4230.LIPIcs.ITCS.2021.78 |
| URL: | |
| الإتاحة: | Open access content. Open access content https://creativecommons.org/licenses/by/3.0 |
| ملاحظة: | application/pdf English |
| أرقام أخرى: | DEDAG oai:drops-oai.dagstuhl.de:13617 doi:10.4230/LIPIcs.ITCS.2021.78 urn:nbn:de:0030-drops-136177 1358728681 |
| المصدر المساهم: | SCHLOSS DAGSTUHL LEIBNIZ ZENTRUM GMBH From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1358728681 |
| قاعدة البيانات: | OAIster |
| DOI: | 10.4230.LIPIcs.ITCS.2021.78 |
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