Yan, Siwen ; Odom, Phillip ; Pasunuri, Rahul ; Kersting, Kristian ; Natarajan, Sriraam (2023)
Learning with privileged and sensitive information: a gradient-boosting approach.
In: Frontiers in Artificial Intelligence, 6
doi: 10.3389/frai.2023.1260583
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
We consider the problem of learning with sensitive features under the privileged information setting where the goal is to learn a classifier that uses features not available (or too sensitive to collect) at test/deployment time to learn a better model at training time. We focus on tree-based learners, specifically gradient-boosted decision trees for learning with privileged information. Our methods use privileged features as knowledge to guide the algorithm when learning from fully observed (usable) features. We derive the theory, empirically validate the effectiveness of our algorithms, and verify them on standard fairness metrics.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Yan, Siwen ; Odom, Phillip ; Pasunuri, Rahul ; Kersting, Kristian ; Natarajan, Sriraam |
| Art des Eintrags: | Bibliographie |
| Titel: | Learning with privileged and sensitive information: a gradient-boosting approach |
| Sprache: | Englisch |
| Publikationsjahr: | 13 November 2023 |
| Ort: | Lausanne |
| Verlag: | Frontiers Media S.A. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Artificial Intelligence |
| Jahrgang/Volume einer Zeitschrift: | 6 |
| Kollation: | 11 Seiten |
| DOI: | 10.3389/frai.2023.1260583 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | We consider the problem of learning with sensitive features under the privileged information setting where the goal is to learn a classifier that uses features not available (or too sensitive to collect) at test/deployment time to learn a better model at training time. We focus on tree-based learners, specifically gradient-boosted decision trees for learning with privileged information. Our methods use privileged features as knowledge to guide the algorithm when learning from fully observed (usable) features. We derive the theory, empirically validate the effectiveness of our algorithms, and verify them on standard fairness metrics. |
| Freie Schlagworte: | privileged information, fairness, gradient boosting, knowledge-based learning, sensitive features |
| ID-Nummer: | Artikel-ID: 1260583 |
| Zusätzliche Informationen: | Erstveröffentlichung; This article is part of the Research Topic: Knowledge-guided Learning and Decision-Making |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Künstliche Intelligenz und Maschinelles Lernen Zentrale Einrichtungen Zentrale Einrichtungen > hessian.AI - Hessisches Zentrum für Künstliche Intelligenz |
| Hinterlegungsdatum: | 08 Mai 2024 12:00 |
| Letzte Änderung: | 08 Mai 2024 12:00 |
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| Suche nach Titel in: | TUfind oder in Google |
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Learning with privileged and sensitive information: a gradient-boosting approach. (deposited 07 Mai 2024 13:17)
- Learning with privileged and sensitive information: a gradient-boosting approach. (deposited 08 Mai 2024 12:00) [Gegenwärtig angezeigt]
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