Identifiability of Model Properties in Over-Parameterized Model Classes

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstrakt

Classical learning theory is based on a tight linkage between hypothesis space (a class of function on a domain X), data space
(function-value examples (x, f(x))), and the space of queries for the learned model (predicting function values for new examples x). However, in many learning scenarios the 3-way association between hypotheses, data, and queries can really be much looser. Model classes can be overparameterized, i.e., different hypotheses may be equivalent with respect to the data observations. Queries may relate to model properties that do not directly correspond to the observations in the data. In this paper we make some initial steps to extend and adapt basic concepts of computational learnability and statistical identifiability to provide a foundation for investigating learnability in such broader contexts. We exemplify the use of the framework in three different applications: the identification of temporal logic properties of probabilistic automata learned from sequence data, the identification of causal dependencies in probabilistic graphical models, and the transfer of probabilistic relational models to new domains.
OriginalsprogEngelsk
TitelMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III
RedaktørerHendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Železný
Antal sider16
UdgivelsesstedBerlin
ForlagSpringer Publishing Company
Publikationsdato2013
Sider112-127
ISBN (Trykt)978-3-642-40993-6
ISBN (Elektronisk)978-3-642-40994-3
DOI
StatusUdgivet - 2013
BegivenhedECML PKDD 2013 - Prag, Tjekkiet
Varighed: 23 sep. 201327 sep. 2013

Konference

KonferenceECML PKDD 2013
LandTjekkiet
ByPrag
Periode23/09/201327/09/2013
NavnLecture Notes in Computer Science
Vol/bind8190
ISSN0302-9743

Fingeraftryk Dyk ned i forskningsemnerne om 'Identifiability of Model Properties in Over-Parameterized Model Classes'. Sammen danner de et unikt fingeraftryk.

Citationsformater