In the database retrieval and nearest neighbor classification tasks, the two basic problems are to represent the query and database objects, and to learn the ranking scores of the database objects to the query. Many studies have been conducted for the representation learning and the ranking score learning problems, however, they are always learned independently from each other. In this paper, w...

Predicate abstraction has become one of the most successful methodologies for proving safety properties of programs. Recently, several abstraction methodologies have been proposed for proving liveness properties. This paper studies “ranking abstraction” where a program is augmented by a nonconstraining progress monitor, and further abstracted by predicate-abstraction, to allow for automatic ver...

Global ranking, a new information retrieval (IR) technology, uses a ranking model for cases in which there exist relationships between the objects to be ranked. In the ranking task, the ranking model is defined as a function of the properties of the objects as well as the relations between the objects. Existing global ranking approaches address the problem by “learning to rank”. In this paper, ...

A contest is a game where several players compete for winning prizes by expending costly efforts. A contest success function determines the probability of winning or losing the contest as a function of these efforts. We assume that the outcome of a contest is an ordered partition of the set of players (a ranking) and a contest success function assigns a probability to each possible outcome. We ...

Although every terminating loop has a ranking function, not every loop has a ranking function of a restricted form, such as a lexicographic tuple of polynomials over program variables. We propose polyranking functions as a generalization of ranking functions for analyzing termination of loops. We define lexicographic polyranking functions in a general context and then specialize their synthesis...

The purpose of this article is to introduce a new scheme for robust multivariate ranking by making use of a not so familiar notion called monotonicity. Under this scheme, as in the case of classical outward ranking, we get an increasing sequence of regions diverging away from a central region (may be a single point) as nucleus. The nuclear region may be deened as the median region.

Most existing learning to rank methods neglect query-sensitive information while producing functions to estimate the relevance of documents (i.e., all examples in the training data are treated indistinctly, no matter the query associated with them). This is counter-intuitive, since the relevance of a document depends on the query context (i.e., the same document may have different relevances, d...

We propose a new algorithm for semi-supervised learning in the bipartite ranking framework. It is based on the maximization of a so-called normalized Rayleigh coefficient, which differs from the usual Rayleigh coefficient of Fisher’s linear discriminant in that the actual covariance matrices are used instead of the scatter matrices. We show that if the class conditional distributions are Gaussi...

Although every terminating loop has a ranking function, not every loop has a ranking function of a restricted form, such as a lexicographic tuple of polynomials over program variables. The polyranking principle is proposed as a generalization of polynomial ranking for analyzing termination of loops. We define lexicographic polyranking functions in the context of loops with parallel transitions ...

The field of information retrieval deals with finding relevant documents from a large document collection or the World Wide Web in response to a user’s query seeking relevant information. Ranking functions play a very important role in the retrieval performance of such retrieval systems and search engines. A single ranking function does not perform well across different user queries, and docume...