543 citations · 1.1k across the 25 of their papers we have counts for
27 papers
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning
Zhen Guo, Zelin Wan, Qisheng Zhang +7
An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex…
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…
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Daniel Cunnington, Alessandra Russo, Mark Law +2
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems re…
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…
Uncertainty-Aware Deep Classifiers using Generative Models
Murat Sensoy, Lance Kaplan, Federico Cerutti +1
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertai…