With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is
imperative that we aggressively investigate fairness and bias in decision-making programs …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Weighted model counting has recently been extended to weighted model integration, which
can be used to solve hybrid probabilistic reasoning problems. Such problems involve both …

Logical AI is concerned with formal languages to represent and reason with qualitative
specifications; statistical AI is concerned with learning quantitative specifications from data …

Weighted model integration (WMI) is a recent formalism generalizing weighted model
counting (WMC) to run probabilistic inference over hybrid domains, characterized by both …

Tasks such as social network analysis, human behavior recognition, or modeling
biochemical reactions, can be solved elegantly by using the probabilistic inference …

Weighted model integration (WMI) extends weighted model counting (WMC) to the
integration of functions over mixed discrete-continuous probability spaces. It has shown …

Weighted model integration (WMI) is a recent formalism generalizing weighted model
counting (WMC) to run probabilistic inference over hybrid domains, characterized by both …

The development of efficient exact and approximate algorithms for probabilistic inference is
a long-standing goal of artificial intelligence research. Whereas substantial progress has …

Weighted model integration (WMI) extends Weighted model counting (WMC) to the
integration of functions over mixed discrete-continuous domains. It has shown tremendous …