Multiple-source adaptation theory and algorithms – addendum
In this note, we present some key results complementing a previous manuscript (Hoffman et al., Ann. Math. Artif. Intell. 89 (3-4), 237–270, 2021 ) dealing with the problem of multiple-source adaptation, a key learning problem in applications. In particular, we extend the theoretical results presente...
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| Veröffentlicht in: | Annals of mathematics and artificial intelligence 2022-06, Vol.90 (6), p.569-572 |
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| container_title | Annals of mathematics and artificial intelligence |
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| creator | Hoffman, Judy Mohri, Mehryar Zhang, Ningshan |
| description | In this note, we present some key results complementing a previous manuscript (Hoffman et al., Ann. Math. Artif. Intell.
89
(3-4), 237–270,
2021
) dealing with the problem of multiple-source adaptation, a key learning problem in applications. In particular, we extend the theoretical results presented for the
probability model
to the case where estimated distributions are used, first by giving a guarantee that depends on the Rényi divergence of the target distribution and the family of mixtures of estimated distributions, next by generalizing that to a result that only depends on the Rényi divergence with respect to the family of mixtures of the exact source distributions. |
| doi_str_mv | 10.1007/s10472-022-09791-5 |
| format | Article |
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89
(3-4), 237–270,
2021
) dealing with the problem of multiple-source adaptation, a key learning problem in applications. In particular, we extend the theoretical results presented for the
probability model
to the case where estimated distributions are used, first by giving a guarantee that depends on the Rényi divergence of the target distribution and the family of mixtures of estimated distributions, next by generalizing that to a result that only depends on the Rényi divergence with respect to the family of mixtures of the exact source distributions.</description><identifier>ISSN: 1012-2443</identifier><identifier>EISSN: 1573-7470</identifier><identifier>DOI: 10.1007/s10472-022-09791-5</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Adaptation ; Algorithms ; Artificial Intelligence ; Complex Systems ; Computer Science ; Estimates ; Mathematics ; Mixtures ; Probability</subject><ispartof>Annals of mathematics and artificial intelligence, 2022-06, Vol.90 (6), p.569-572</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022</rights><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-c25e6d8df4eee49a7e5020b83b2349210efbe104c8c2769b8910e681521e7e4b3</citedby><cites>FETCH-LOGICAL-c319t-c25e6d8df4eee49a7e5020b83b2349210efbe104c8c2769b8910e681521e7e4b3</cites><orcidid>0000-0002-3987-9847</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10472-022-09791-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918205019?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Hoffman, Judy</creatorcontrib><creatorcontrib>Mohri, Mehryar</creatorcontrib><creatorcontrib>Zhang, Ningshan</creatorcontrib><title>Multiple-source adaptation theory and algorithms – addendum</title><title>Annals of mathematics and artificial intelligence</title><addtitle>Ann Math Artif Intell</addtitle><description>In this note, we present some key results complementing a previous manuscript (Hoffman et al., Ann. Math. Artif. Intell.
89
(3-4), 237–270,
2021
) dealing with the problem of multiple-source adaptation, a key learning problem in applications. In particular, we extend the theoretical results presented for the
probability model
to the case where estimated distributions are used, first by giving a guarantee that depends on the Rényi divergence of the target distribution and the family of mixtures of estimated distributions, next by generalizing that to a result that only depends on the Rényi divergence with respect to the family of mixtures of the exact source distributions.</description><subject>Adaptation</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Complex Systems</subject><subject>Computer Science</subject><subject>Estimates</subject><subject>Mathematics</subject><subject>Mixtures</subject><subject>Probability</subject><issn>1012-2443</issn><issn>1573-7470</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kDtOxDAQhi0EEsvCBagiURvGjh3bBQVa8ZIW0UBtOclkN6u8sJNiO-7ADTkJhiDRUYxmNPr_eXyEnDO4ZADqKjAQilPgMYwyjMoDsmBSpVQJBYexBsYpFyI9Jich7ADAZDpbkOunqRnroUEa-skXmLjSDaMb675Lxi32fp-4rkxcs-l9PW7bkHy-f0RRiV05tafkqHJNwLPfvCSvd7cvqwe6fr5_XN2saZEyM9KCS8xKXVYCEYVxCiVwyHWa81QYzgCrHOMHhS64ykyuTWxlmknOUKHI0yW5mOcOvn-bMIx2F6_t4krLDdMcJDATVXxWFb4PwWNlB1-3zu8tA_uNyc6YbMRkfzBZGU3pbApR3G3Q_43-x_UFgrtq4Q</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Hoffman, Judy</creator><creator>Mohri, Mehryar</creator><creator>Zhang, Ningshan</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-3987-9847</orcidid></search><sort><creationdate>20220601</creationdate><title>Multiple-source adaptation theory and algorithms – addendum</title><author>Hoffman, Judy ; 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89
(3-4), 237–270,
2021
) dealing with the problem of multiple-source adaptation, a key learning problem in applications. In particular, we extend the theoretical results presented for the
probability model
to the case where estimated distributions are used, first by giving a guarantee that depends on the Rényi divergence of the target distribution and the family of mixtures of estimated distributions, next by generalizing that to a result that only depends on the Rényi divergence with respect to the family of mixtures of the exact source distributions.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10472-022-09791-5</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-3987-9847</orcidid></addata></record> |
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| source | ProQuest Central UK/Ireland; SpringerLink Journals - AutoHoldings; ProQuest Central |
| subjects | Adaptation Algorithms Artificial Intelligence Complex Systems Computer Science Estimates Mathematics Mixtures Probability |
| title | Multiple-source adaptation theory and algorithms – addendum |
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