Fairness is increasingly recognized as a critical component of machine learning systems.
However, it is the underlying data on which these systems are trained that often reflect …

Fairness is increasingly recognized as a critical component of machine learning systems.
However, it is the underlying data on which these systems are trained that often reflect …

In this paper, we present a Web-based intelligent tutoring system, called BITS. The decision
making process conducted in our intelligent system is guided by a Bayesian network …

Web Intelligence is a direction for scientific research that explores practical applications of
Artificial Intelligence to the next generation of Web-empowered systems. In this paper, we …

Statistical Constraints (SCs) play an important role in statistical modeling and analysis. This
paper brings the concept to data cleaning and studies how to leverage SCs for error …

We investigate the implication problem for classes of data dependencies over SQL table
definitions. Under Zaniolo's “no information” interpretation of null markers we establish an …

Team semantics is a semantical framework for the study of dependence and independence
concepts ubiquitous in many areas such as databases and statistics. In recent works team …

We resolve three long-standing open problems, namely the (algorithmic) decidability of
network coding, the decidability of conditional information inequalities, and the decidability …

We define a variant of team semantics called multiteam semantics based on multisets and
study the properties of various logics in this framework. In particular, we define natural …

Directed acyclic graphs (DAGs) constitute a qualitative representation for conditional
independence (CI) properties of a probability distribution. It is known that every CI statement …