Evidential Learning and Reasoning (ELR)
Evidential Learning and Reasoning aims at providing uncertainty-aware learning and reasoning capabilities to AI systems, so to encompass both epistemic and aleatory uncertainty.
For further details, please see the following tutorials:
ELR features in
- Enabling rapidly formed human-agent coalition teams through extensible information exchange;
- Uncertainty-Aware AI&ML.
It comprises research both in Evidential Deep Learning (EDL) and Evidential Logic Programming (ELP).
Relevant ELR publications are:
- Murat Sensoy, Lance Kaplan, Melih Kandemir, Evidential Deep Learning to Quantify Classification Uncertainty, NeurIPS 2018, 2018
- Federico Cerutti, Lance Kaplan, Angelika Kimmig, Murat Sensoy, Probabilistic Logic Programming with Beta-Distributed Random Variables, AAAI 2019, 2019.
- Murat Sensoy, Lance Kaplan, Federico Cerutti, Maryam Saleki, Uncertainty-Aware Deep Classifiers using Generative Models, AAAI 2020, 2020
- Lance Kaplan, Federico Cerutti, Murat Sensoy, Kumar Vijay Mishra, Second-Order Learning and Inference using Incomplete Data for Uncertain Bayesian Networks: A Two Node Example, Fusion 2020, 2020.
- Federico Cerutti, Lance Kaplan, Angelika Kimmig, Murat Sensoy, Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits, Submitted, TBD.