Titre :

KSD Aggregated Goodness-of-fit Test

Auteur(s) :

Schrab, Antonin [Auteur]
MOdel for Data Analysis and Learning [MODAL]
The Inria London Programme [Inria-London]
Department of Computer science [University College of London] [UCL-CS]
Gatsby Computational Neuroscience Unit
Guedj, Benjamin [Auteur]
MOdel for Data Analysis and Learning [MODAL]
The Inria London Programme [Inria-London]
The Alan Turing Institute
Department of Computer science [University College of London] [UCL-CS]
Gretton, Arthur [Auteur]
Gatsby Computational Neuroscience Unit

Titre de la manifestation scientifique :

Conference on Neural Information Processing Systems

Ville :

New Orleans

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2022-11-28

Titre de l’ouvrage :

PMLR

Discipline(s) HAL :

Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Théorie [stat.TH]

Résumé en anglais : [en]

We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAGG, which aggregates multiple tests with different kernels. KSDAGG ...
Lire la suite >We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAGG, which aggregates multiple tests with different kernels. KSDAGG avoids splitting the data to perform kernel selection (which leads to a loss in test power), and rather maximises the test power over a collection of kernels. We provide theoretical guarantees on the power of KSDAGG: we show it achieves the smallest uniform separation rate of the collection, up to a logarithmic term. KSDAGG can be computed exactly in practice as it relies either on a parametric bootstrap or on a wild bootstrap to estimate the quantiles and the level corrections. In particular, for the crucial choice of bandwidth of a fixed kernel, it avoids resorting to arbitrary heuristics (such as median or standard deviation) or to data splitting. We find on both synthetic and real-world data that KSDAGG outperforms other state-of-the-art adaptive KSDbased goodness-of-fit testing procedures.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Commentaire :

9 pages

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL