52 citations · 92 across the 8 of their papers we have counts for
6 papers · 1 filter
Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits
Federico Cerutti, Lance M. Kaplan, Angelika Kimmig +1
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may…
A General Framework for Distributed Inference with Uncertain Models
James Z. Hare, Cesar A. Uribe, Lance Kaplan +1
This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best descr…
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
Marc Roig Vilamala, Harrison Taylor, Tianwei Xing +6
Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is a…
Non-Bayesian Social Learning with Uncertain Models
James Z. Hare, Cesar A. Uribe, Lance Kaplan +1
Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network. In this framework, each agent it…
Uncertainty Aware AI ML: Why and How
Lance Kaplan, Federico Cerutti, Murat Sensoy +2
This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of mo…
Probabilistic Logic Programming with Beta-Distributed Random Variables
Federico Cerutti, Lance Kaplan, Angelika Kimmig +1
We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve th…