I. Whitehouse, H. Kang and W. Losert “Emergent Detection of Concept Drift within the Glia-Inspired `Rhythmic Sharing’ Algorithm,” preprint (under review), 2026. Download
R. O’Loughlin, B. Oripov, N. Skuda, N. Chongsiriwatana, I. Whitehouse, W. Losert, B. Hayes, A. McCaughan and S. Buckley, “δ Multiplexed Gradient Descent: Perturbative Learning with Astrocytes,” in Proceedings of the IEEE Conference on Neuro-Inspired Computational Elements (NICE), Atlanta, United States, 2026.
I. Whitehouse, R. Yepez-Lopez and R. Corizzo, “Distributed Concept Drift Detection for Efficient Model Adaptation with Big Data Streams,” in Proceedings of the 2023 IEEE International Conference on Big Data, Sorrento, Italy, 2023. DOI, Download
L. P. Damasceno, E. Rexhepi, A. Shafer, I. Whitehouse, N. Japkowicz, C. C. Cavalcante, R. Corizzo and Z. Boukouvalas, “Exploiting Sparsity and Statistical Dependence in Multivariate Data Fusion: An Application to Misinformation Detection for High-Impact Events,” Machine Learning, vol. 112, 2023. DOI, Download
L. P. Damasceno, E. Rxhepi, A. Shafer, I. Whitehouse, C. C. Cavalcante, R. Corizzo and Z. Boukouvalas, “Independent Vector Analysis with Sparse Inverse Covariance Estimation: An Application to Misinformation Detection,” in Proceedings of the IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), Rome, 2023. DOI, Download