Results 11 to 20 of about 12,421,662 (339)

On Theoretically Optimal Ranking Functions in Bipartite Ranking [PDF]

open access: yesJournal of the American Statistical Association, 2017
This article investigates the theoretical relation between loss criteria and the optimal ranking functions driven by the criteria in bipartite ranking.
Kazuki Uematsu, Yoonkyung Lee
exaly   +4 more sources

On the ERA ranking representability of pairwise bipartite ranking functions

open access: yesArtificial Intelligence, 2011
In domains like decision theory and social choice theory it is known for a long time that stochastic transitivity properties yield necessary and sufficient conditions for the ranking or utility representability of reciprocal preference relations. In this
Willem Waegeman, Bernard De Baets
exaly   +5 more sources

Eventual linear ranking functions [PDF]

open access: yesProceedings of the 15th Symposium on Principles and Practice of Declarative Programming, 2013
Program termination is a hot research topic in program analysis. The last few years have witnessed the development of termination analyzers for programming languages such as C and Java with remarkable precision and performance.
Roberto Bagnara, F. Mesnard
semanticscholar   +5 more sources

Ranking functions and rankings on languages [PDF]

open access: yesArtificial Intelligence, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Franz Huber
exaly   +5 more sources

Synthesis of ranking functions using extremal counterexamples

open access: yesACM SIGPLAN Notices, 2015
We present a complete method for synthesizing lexicographic linear ranking functions (and thus proving termination), supported by inductive invariants, in the case where the transition relation of the program includes disjunctions and existentials (large
Laure Gonnord, David Monniaux
exaly   +4 more sources

On Multiphase-Linear Ranking Functions [PDF]

open access: yesInternational Conference on Computer Aided Verification, 2017
Multiphase ranking functions (\( M\varPhi \)RFs) were proposed as a means to prove the termination of a loop in which the computation progresses through a number of “phases”, and the progress of each phase is described by a different linear ranking ...
Amir M. Ben-Amram, S. Genaim
semanticscholar   +3 more sources

Ranking with submodular functions on a budget [PDF]

open access: yesData Mining and Knowledge Discovery, 2022
AbstractSubmodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items.
Guangyi Zhang 0001   +2 more
openaire   +5 more sources

Ranking Functions for Size-Change Termination II [PDF]

open access: yesLogical Methods in Computer Science, 2009
Size-Change Termination is an increasingly-popular technique for verifying program termination. These termination proofs are deduced from an abstract representation of the program in the form of "size-change graphs".
Amir M. Ben-Amram, Chin Soon Lee
doaj   +2 more sources

A Theory for Ranking Distribution Functions [PDF]

open access: yesSSRN Electronic Journal, 2013
When is one distribution (of income, consumption, or some other economic variable) more equal or better than another? This question has proven difficult to answer in situations where distribution functions intersect and no unambiguous ranking can be attained without introducing weaker criteria than second-degree stochastic dominance.
Aaberge, Rolf   +2 more
openaire   +9 more sources

Supermartingales, Ranking Functions and Probabilistic Lambda Calculus [PDF]

open access: yesLogic in Computer Science, 2021
We introduce a method for proving almost sure termination in the context of lambda calculus with continuous random sampling and explicit recursion, based on ranking supermartingales. This result is extended in three ways.
Andrew Kenyon-Roberts, C. Ong
semanticscholar   +1 more source

Home - About - Disclaimer - Privacy