@conference {471,
	title = {On The Relationship Between Online Gaussian Process Regression And Kernel Least Mean Squares Algorithms},
	booktitle = {2016 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016)},
	year = {2016},
	month = {September},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Salerno, Italy},
	abstract = {We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS algorithms correspond to specific cases of this model. The probabilistic perspective allows us to understand how each of them handles uncertainty, which could explain some of their performance differences.},
	keywords = {gaussian processes, kernel adaptive filtering, KLMS},
	author = {Van Vaerenbergh, Steven and Fern{\'a}ndez-Bes, Jes{\'u}s and Elvira, Victor}
}
@conference {431,
	title = {A Probabilistic Least-Mean-Squares Filter},
	booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2015},
	month = {April},
	address = {Brisbane, Queensland, Australia},
	abstract = {We introduce a probabilistic approach to the LMS filter. By means of an efficient approximation, this approach provides an adaptable step-size LMS algorithm together with a measure of uncertainty about the estimation. In addition, the proposed approximation preserves the linear complexity of the standard LMS. Numerical results show the improved performance of the algorithm with respect to standard LMS and state-of-the-art algorithms with similar complexity. The goal of this work, therefore, is to open the door to bring some more Bayesian machine learning techniques to adaptive filtering.},
	keywords = {adaptive filtering, least-mean-squares, probabilistic models, state-space models},
	doi = {10.1109/ICASSP.2015.7178361},
	author = {Fern{\'a}ndez-Bes, Jes{\'u}s and Elvira, Victor and Van Vaerenbergh, Steven}
}
