4 papers
Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Kurt Butler, Guanchao Feng, Petar Djuric
Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation…
Model Proficiency in Centralized Multi-Agent Systems: A Performance Study
Anna Guerra, Francesco Guidi, Pau Closas +2
Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and team-level proficiency. While profici…
Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
Marzieh Ajirak, Anand Ravishankar, Petar M. Djuric
Uncertainty Quantification (UQ) is essential in probabilistic machine learning models, particularly for assessing the reliability of predictions. In this paper, we present a system…
Trustworthy Prediction with Gaussian Process Knowledge Scores
Kurt Butler, Guanchao Feng, Tong Chen +1
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are we…