User-friendly introduction to PAC-Bayes bounds

User-friendly introduction to PAC-Bayes bounds

1. Introduction2. First Step in the PAC-Bayes World3. Tight and Non-vacuous PAC-Bayes Bounds4. PAC-Bayes Oracle Inequalities and Fast Rates5. Beyond Bounded Loss and i.i.d. Observations 6. Related Approaches in Statistics and Machine Learning Theory7. ConclusionAcknowledgementsReferences

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Bibliographische Detailangaben
1. Verfasser: Alquier, Pierre (VerfasserIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: Boston, Delft now 2024
Schriftenreihe:Foundations and trends in machine learning volume 17, issue 2 (2024)
Schlagworte:
Bayesianische Inferenz > COMPUTERS / Machine Theory > Informationstechnik (IT), allgemeine Themen > Informationstheorie > Machine learning > Maschinelles Lernen > Maschinelles Sehen, Bildverstehen > Theoretische Informatik > Wissensbasierte Systeme, Expertensysteme > Deep learning
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Zusammenfassung:1. Introduction2. First Step in the PAC-Bayes World3. Tight and Non-vacuous PAC-Bayes Bounds4. PAC-Bayes Oracle Inequalities and Fast Rates5. Beyond Bounded Loss and i.i.d. Observations 6. Related Approaches in Statistics and Machine Learning Theory7. ConclusionAcknowledgementsReferences
Covering both empirical and oracle PAC-bounds, this book serves as a primer for students and researchers who want to get to grips quickly with the subject
Beschreibung:Literaturverzeichnis: Seite 113-135
Beschreibung:135 Seiten
ISBN:9781638283263
978-1-63828-326-3