Machine Learning and Bayesian Inference: Publications

Graph Convolutional Neural Networks

• S. Pal, F. Regol and M.J. Coates, Bayesian graph convolutional neural networks using node copying, in Proc. Learning and Reasoning with Graph-Structured Representations Workshop, (International Conference on Machine Learning), Long Beach, USA, Jun. 2019. Software available here!!
• S. Pal, F. Regol and M.J. Coates, Bayesian graph convolutional neural networks using non-parametric graph learning, in Proc. Representation Learning on Graphs and Manifolds Workshop (Int. Conf. Learning Representations), New Orleans, USA, May 2019.
• Y. Zhang, S. Pal, D. Üstebay and M.J. Coates, Bayesian graph convolutional neural networks for semi-supervised classification, in Proc. AAAI Int. Conf. Artificial Intelligence, Hawaii, USA, Feb. 2019. Software available here!!
• J. Valenchon and M.J. Coates, Multiple-graph recurrent graph convolutional neural network architectures for predicting disease outcomes, in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, Brighton, UK, May 2019.

Robust and Sparse Multivariate Regression

• M. Kharratzadeh and M. Coates, Sparse multivariate factor regression, arXiv:1502.07334, 2017. Matlab code available here!!
• M. Kharratzadeh and M. Coates, Semi-parametric order-based generalized multivariate regression, J. Multivariate Analysis, vol. 156, pp. 89-102, Apr. 2017.
• M. Kharratzadeh and M.J. Coates, Order-based generalized multivariate regression, in Proc. IEEE Stat. Signal Processing Workshop, Palma de Mallorca, Spain, Jun. 2016.
• M. Kharratzadeh and M.J. Coates, Sparse multivariate factor regression, in Proc. IEEE Stat. Signal Processing Workshop, Palma de Mallorca, Spain, Jun. 2016.

Inferring Diffusions on Graphs and Networks

• S. Shaghaghian and M. Coates, Online Bayesian inference of diffusion networks, IEEE Trans. Signal and Information Processing over Networks, vol. 3, no. 3, pp. 500-512, Sept. 2017.
• S. Shaghaghian and M.J. Coates, Bayesian inference of diffusion networks with unknown infection times, in Proc. IEEE Stat. Signal Processing Workshop, Palma de Mallorca, Spain, Jun. 2016.

Online and Time-dependent Clustering

• M. Kharratzadeh, B. Renard, and M.J. Coates, Bayesian topic model approaches to online and time-dependent clustering, Digital Signal Processing, vol. 47, no. 12, pp. 25-35, 2015.
• B. Renard, M. Kharratzadeh, and M.J. Coates, Online time-dependent clustering using probabilistic topic models, in Proc. IEEE Int. Conf. Acoustics, Speech and Sig. Proc. (ICASSP), Brisbane, Australia, Apr. 2015.

Graph Clustering

• S.S. Tabatabaei, M. Coates, and M. Rabbat, GANC: Greedy agglomerative normalized cut, Pattern Recognition, vol. 45, no. 2, pp. 831-843, February, 2012. Matlab and C++ code.

False Discovery Rate Controlled Multi-label Classification

• D. Nechay, Y. Pointurier and M.J. Coates, Controlling false alarm/discovery rates in online Internet traffic flow classification, in Proc. IEEE Infocom, Rio de Janeiro, Brazil, April 2009.

Hierarchical Clustering

• R. M. Castro, M. J. Coates, and R.D. Nowak, Likelihood based hierarchical clustering, IEEE Trans. Signal Processing,vol. 52, no. 8, pp. 2308-2321, Aug. 2004.

Learning to Satisfy: Maximizing the volume of a set while ensuring criteria are satisfied

• F. Thouin, M.J. Coates, B. Erikkson, R. Nowak, C. Scott, Learning to satisfy, in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, NV, USA, Apr. 2008.

Online Anomaly Detection

• T. Ahmed, M. Coates and A. Lakhina, Multivariate online anomaly detection using kernel recursive least squares, in Proc. IEEE Infocom, Anchorage, AK, May 2007.
• T. Ahmed, B. Oreshkin and M.J. Coates, Machine learning approaches to network anomaly detection, in Proc. SysML, Boston MA, April 2007.