ITEM METADATA RECORD
Title: k-optimal: A novel approximate inference algorithm for ProbLog
Authors: Renkens, Joris ×
Van den Broeck, Guy
Nijssen, Siegfried #
Issue Date: 27-Jul-2012
Publisher: Springer New York LLC
Series Title: Machine Learning vol:89 issue:3 pages:215-231
Article number: 10.1007/s10994-012-5304-9
Abstract: ProbLog is a probabilistic extension of Prolog. Given the complexity of exact inference under ProbLog’s semantics, in many applications in machine learning approximate inference is necessary. Current approximate inference algorithms for ProbLog however require either dealing with large numbers of proofs or do not guarantee a low approximation error. In this paper we introduce a new approximate inference algorithm which addresses these shortcomings. Given a user-specified parameter k, this algorithm approximates the
success probability of a query based on at most k proofs and ensures that the calculated probability p is (1 − 1/e)p ∗ ≤ p ≤ p ∗ , where p ∗ is the highest probability that can be calculated based on any set of k proofs. Furthermore a useful feature of the set of calculated proofs is that it is diverse. Our experiments show the utility of the proposed algorithm.
ISSN: 0885-6125
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
paper.pdfk-optimal Published 445KbAdobe PDFView/Open

 


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science