Senior Lecturer in Machine Learning
Department of Computer Science
University of Manchester
Manchester, UK
Publications
Teaching
Research Assistants and Students
Book and journal clubs
J. Gil-González, Juan-José Giraldo, A. M. Álvarez-Meza, A. Orozco-Gutiérrez, M. A. Álvarez, Correlated Chained Gaussian Processes for Datasets With Multiple Annotators, IEEE Transactions on Neural Networks and Learning Systems, [doi].
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez and M. A. Álvarez, Modular Gaussian processes, NeurIPS 2022, [abs].
Thomas McDonald and M. A. Álvarez, Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features, NeurIPS 2021, [abs], [arxiv].
Magnus Ross, Mike Smith, M. A. Álvarez, Learning Nonparametric Volterra Kernels with Gaussian Processes, NeurIPS 2021, [abs], [arxiv].
Mike Smith, M. A. Álvarez, N. Lawrence, Differentially Private Regression and Classification with Sparse Gaussian Processes, Journal of Machine Learning Research, [abs].
Jean-Charles Croix and Nicolas Durrande and M. A Álvarez, Bayesian inversion of a diffusion model with application to Biology, Journal of Mathematical Biology, [arxiv], [pdf].
Juan-José Giraldo and M. A. Álvarez, A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model, IEEE Transactions on Neural Networks and Learning Systems, [arxiv], [doi].
James Marulanda-Durango, Andrés Escobar-Mejía, and Alfonso Alzate-Gómez and M. A. Álvarez, A Support Vector Machine-Based Method for Parameter Estimation of an Electric Arc Furnace Model, [doi].
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez and M. A. Álvarez, Recyclable Gaussian Processes, [arxiv].
Virginia Aglietti, Theodoros Damoulas, M. A. Álvarez and Javier González, Multi-task Causal Learning with Gaussian Processes, NeurIPS 2020, [arxiv].
Cristian Torres-Valencia, Álvaro A Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza and M. A. Álvarez, A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis, Applied Sciences [doi].
Wil Ward, Tom Ryder, Dennis Prangle and M. A. Álvarez, Black-box Inference for Non-linear Latent Force Models, AISTATS 2020 [jmlr] [arxiv]
Juan-José Giraldo and M. A. Álvarez, A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model, [arxiv].
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez and M. A. Álvarez, Continual Multi-task Gaussian Processes [arxiv].
F. Yousefi, M. Smith and M. A. Álvarez, Multi-task Learning for Aggregated Data using Gaussian Processes, NeurIPS 2019 [NeurIPS Link]
A. F. López-Lopera, N. Durrande and M. A. Álvarez, Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster, IEEE/ACM Transactions on Computational Biology and Bioinformatics, [arxiv, doi].
P. Alvarado, M. A. Álvarez and Dan Stowell, Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain, ICASSP 2019, [arxiv, doi]
M. A. Álvarez, W. Ward and C. Guarnizo, Non-linear process convolutions for multi-output Gaussian processes, AISTATS 2019, [pdf].
H. Vargas, A. Álvarez, Á. Orozco and M. A. Álvarez, Tensor decomposition processes for interpolation of diffusion magnetic resonance imaging, Expert Systems with Applications, [doi]
S. Särkkä, M. A. Álvarez and N. D. Lawrence, Gaussian process latent force models for learning and stochastic control of physical systems, IEEE Transactions on Automatic Control, [arxiv, doi]
M. Smith, N. Lawrence and M. A. Álvarez, Gaussian Process Regression for Binned Data, [arxiv]
Hernán F. García, Álvaro Orozco and M. A. Álvarez, Nonlinear Probabilistic Latent Variable Models for Groupwise Correspondence Analysis in Brain Structures, MLSP 2018 [doi]
C. Zuluaga and M. A. Álvarez, Bayesian Probabilistic Power Flow Analysis Using Jacobian Approximate Bayesian Computation, IEEE Transactions on Power Systems [doi]
C. D. Guarnizo, M. A. Álvarez, Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian Processes, at UAI 2018 [pdf]
M. Smith, M. A. Álvarez, M. Zwiessele N. D. Lawrence, Differentially Private Regression with Gaussian processes, AISTATS 2018 [abs, pdf, arxiv]
Jean-Charles Croix, Nicolas Durrande and M. A. Álvarez, Bayesian inversion of a diffusion evolution equation with application to Biology [arxiv]
P. Moreno-Muñoz, A. Artés-Rodríguez and M. A. Álvarez, Heterogeneous Multi-output Gaussian Process Prediction, NeurIPS 2018 [abs, arxiv, pdf, code]
H. F. García, M. A. Álvarez and A. A. Orozco, Dynamic Facial Landmarking Selection for Emotion Recognition using Gaussian Processes, Journal on Multimodal User Interfaces [doi]
A. F. López-Lopera and M. A. Álvarez, Switched latent force models for reverse-engineering transcriptional regulation in gene expression data, IEEE/ACM Transactions on Computational Biology and Bioinformatics [doi]
E. A. Valencia and M. A. Álvarez, Short-term time series prediction using Hilbert space embeddings of autoregressive processes, Neurocomputing [doi]
H. D. Vargas, M. A. Álvarez and A. A. Orozco, Multi-task learning for subthalamic nucleus identification in deep brain stimulation, International Journal of Machine Learning and Cybernetics [doi]
Z. Dai, M. A. Álvarez, N. Lawrence, Efficient modeling of latent information in supervised learning using Gaussian processes, at NIPS 2017 [abs, pdf, arxiv]
C. Zuluaga, M. A. Álvarez, Approximate Probabilistic Power Flow, at 4th ECML PKDD Workshop, DARE 2016, Riva del Garda, Italy, September 23, 2016 [doi]
V. Gómez-Orozco, J. Cuellar, H. F. García, A. Álvarez, M. A. Álvarez, A. Orozco, O. Henao, A Kernel-Based Approach for DBS Parameter Estimation, CIARP 2016, pp 249-256, 2016 [doi]
H. F. García, M. A. Álvarez, A. A. Orozco, Bayesian Optimization for Fitting 3D Morphable Models of Brain Structures, CIARP 2016, pp 249-256, 2016 [doi]
D. Agudelo-España, M. A. Álvarez, A. A. Orozco, Definition and Composition of Motor Primitives Using Latent Force Models and Hidden Markov Models, CIARP 2016, pp 249-256, 2016 [doi]
C. A. Torres, M. A. Álvarez and A. A. Orozco, SVM-based feature selection methods for emotion recognition from multimodal data, Journal on Multimodal User Interfaces, Vol 2016, pp 1-15 [doi]
P. A. Alvarado, C. A. Torres and A. A. Orozco, M. A. Álvarez, G. Daza, and H. Carmona, Model and behavior of the simulation of electric propagation during deep brain stimulation, DYNA 83 (198) pp. 49-58, 2016 [doi]
H. D. Vargas, A. A. Orozco, M. A. Álvarez, Multi-output Gaussian processes for enhancing resolution of diffusion tensor fields, at EMBC 2016 [doi]
H. F. García, M. A. Álvarez, A. A. Orozco, Gaussian process dynamical models for multimodal affect recognition, at EMBC 2016, [doi]
E. A. Valencia and M. A. Álvarez, Short-term time series prediction using Hilbert space embeddings of autoregressive processes [arxiv]
V. Gómez, M. A. Álvarez, O. Henao, G. Daza, and A. A. Orozco, Estimation of the neuromodulation parameters from the planned volume of tissue activated in deep brain stimulation, Journal of the School of Engineering of the Antioquia University, Colombia [doi]
C. D. Zuluaga, M. A. Álvarez and E. Giraldo, Short-term wind speed prediction based on robust Kalman filtering: an experimental comparison, Applied Energy, Vol 156, Oct 2015, 321–330 [doi]
H. F. García, M. A. Álvarez, A. A. Orozco, Groupwise shape correspondences on 3D brain structures using Probabilistic Latent Variable Models, at ISVC 2015 [doi]
H. D. Vargas, M. A. Álvarez, A. A. Orozco, Generalized Wishart processes for interpolation over diffusion tensor fields, at ISVC 2015 [whiterose]
H. D. Vargas, A. F. López, A. A. Orozco, M. A. Álvarez, N. Malpica, J. A. Hernández, Gaussian processes for slice-based super-resolution MR images, at ISVC 2015 [doi]
S. Gómez, M. A. Álvarez, H. F. García, J. I. Ríos, A. A. Orozco, Discriminative Training for Convolved Multiple-Output Gaussian Processes, at CIARP 2015 [doi]
C. Guarnizo, M. A. Álvarez, A. A. Orozco, Indian Buffet process for model selection in latent force models, at CIARP 2015 [doi]
M. Orbes-Arteaga, D. Cárdenas-Peña, M. A. Álvarez, A. Orozco, G. Castellanos, Magnetic Resonance Image Selection for Multi-Atlas Segmentation using Mixture Models, at CIARP 2015 [doi]
C. D. Zuluaga, E. Valencia, M. A. Álvarez, A. A. Orozco, A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation, at ICIAP 2015 [doi]
H. D. Vargas, M. A. Álvarez, A. A. Orozco, Convolved multi-output Gaussian processes for Semi-Supervised Learning, at ICIAP 2015 [doi]
J. Y. Cuesta, M. A. Álvarez, A. A. Orozco, Global and Local Gaussian Process for Multioutput and Treed Data, at ICIAP 2015 [doi]
J. J. Giraldo, M. A. Álvarez, A. A. Orozco, Peripheral Nerve Segmentation Using Nonparametric Bayesian Hierarchical Clustering, at EMBC 2015 [doi]
J. Gil González, M. A. Álvarez, A. A. Orozco, Automatic Segmentation of Nerve Structures in Ultrasound Images Using Graph Cuts and Gaussian Processes, at EMBC 2015 [doi]
J. Gil González, M. A. Álvarez, A. A. Orozco, Automatic Assessment of Voice Quality in the Context of Multiple Annotations, at EMBC 2015 [doi]
J. J. Giraldo, H. F. García, M. A. Álvarez, A. A. Orozco, D. Salazar, Peripheral Nerve Segmentation Using Speckle Removal and Bayesian Shape Models, at IbPRIA 2015 [doi]
M. Orbes-Arteaga, D. Cárdenas-Peña, M. A. Álvarez, A. Orozco, G. Castellanos, Spatial-dependent Similarity Metric supporting Multi-Atlas MRI Segmentation, at IbPRIA 2015 [doi]
I. de la Pava, V. Gómez, M. A. Álvarez, O. A. Henao, G. Daza-Santacoloma, A. Orozco, A Gaussian Process Emulator for Estimating the Volume of Tissue Activated During Deep Brain Stimulation, at IbPRIA 2015 [doi]
A. F. López, M. A. Álvarez, A. A. Orozco, Improving Diffusion Tensor Estimation using Adaptive and Optimized Filtering based on Local Similarity, at IbPRIA 2015 [doi]
A. F. López-Lopera and M. A. Álvarez, Switched Dynamical Latent Force Models for Modelling Transcriptional Regulation [arxiv]
A. F. López-Lopera, M. A. Álvarez, A. A. Orozco, Sparse Linear Models applied to Power Quality Disturbance Classification [arxiv]
C. D. Zuluaga, E. A. Valencia and M. A. Álvarez, A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation [arxiv]
C. D. Guarnizo and M. A. Álvarez, Indian Buffet process for model selection in Convolved multiple-output Gaussian processes [arxiv]
S. Gómez-González, M. A. Álvarez and H. F. García, Discriminative training for Convolved multiple-output Gaussian processes [arxiv]
C. D. Zuluaga, E. Giraldo and M. A. Álvarez, Robust statistics dual Kalman filter for wind generator identification. Revista Ingeniería y Desarrollo, Vol 32, No 1, pp 115–137 [pdf]
H. F. García, M. A. Álvarez, A. A. Orozco, Bayesian Shape Models With Shape Priors for MRI Brain Segmentation, at ISCV 2014 [doi]
H. F. García, M. A. Álvarez, A. A. Orozco, Gaussian process dynamical models for emotion recognition, at ISCV 2014 [doi]
P. A. Alvarado, M. A. Álvarez, G. Daza-Santacoloma, A. Orozco, G. Castellanos, A latent force model for describing electric propagation in deep brain stimulation: a simulation study, at EMBC 2014 [doi]
C. A. Torres, M. A. Álvarez and A. Orozco, Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment, at EMBC 2014 [doi]
J. D. Vásquez, M. A. Álvarez and A. Orozco, Latent force models for describing transcriptional regulation processes in the embryo development problem for Drosophila melanogaster, at EMBC 2014 [doi]
M. A. Álvarez, D. Luengo and N. D. Lawrence, Linear Latent Force Models using Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 35, No 11, pp 2693–2705 [doi]
C. A. Torres, A. A. Orozco and M. A. Álvarez, Feature Selection for Multimodal Emotion Recognition in the Arousal-Valence Space, at EMBC 2013 [doi]
H. F. García, A. A. Orozco, and M. A. Álvarez, Dynamic physiological signal analysis based on Fisher kernels for emotion recognition, at EMBC 2013 [doi]
M. A. Álvarez, L. Rosasco and N. D. Lawrence, Kernels for Vector-Valued Functions: A Review. Foundations and Trends® in Machine Learning: Vol. 4: No 3, pp 195–266 [doi]
H. D. Vargas, A. A. Orozco, and M. A. Álvarez, Multi-patient Learning increases accuracy for Subthalamic nucleus identification in deep brain stimulation, at EMBC 2012 [doi]
H. D. Vargas, J. B. Padilla, R. Arango, H. Carmona, M. A. Álvarez, E. Guijarro and A. A. Orozco, NEUROZONE: On-line Recognition of Brain Structures in Stereotactic Surgery - Application to Parkinson’s Disease, at EMBC 2012 [doi]
M. A. Álvarez and N. D. Lawrence, Computationally efficient convolved multiple output Gaussian processes, Journal of Machine Learning Research 12, pp 1459–1500 [pdf]
M. A. Álvarez, D. Luengo and N. D. Lawrence, Linear Latent Force Models using Gaussian Processes [arxiv)
M. A. Álvarez, L. Rosasco and N. D. Lawrence, Kernels for vector-valued functions: a review [arxiv]
J.F. Vargas, M. A. Álvarez, M. Orozco-Alzate and C. G. Castellanos, Teoría de Señales: Fundamentos, Sección de Publicaciones e Imagen, Universidad Nacional de Colombia, Sede Manizales, Noviembre 2010, Primera Edición, ISBN 978-958-8280-43-1
M. A. Álvarez, J. Peters, B. Schölkopf and N. D. Lawrence (2011): Switched latent force models for movement segmentation, at NIPS 2010 [pdf]
M. A. Álvarez, D. Luengo, M. Titsias and N. Lawrence (2010): Efficient Multioutput Gaussian Processes through Variational Inducing Kernels, at AISTATS 2010 [pdf]
M. A. Álvarez, D. Luengo, M. K. Titsias, N. D. Lawrence (2010): Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes [arxiv]
M. A. Álvarez, D. Luengo and N. Lawrence, Latent Force Models,at AISTATS 2009 [pdf]
M. A. Álvarez and N. D. Lawrence, Sparse convolved multiple output Gaussian processes [arxiv]
M. A. Álvarez and N. Lawrence, Sparse Convolved Gaussian Processes for Multi-output Regression, at NIPS 2018 [pdf]
M. A. Álvarez and R. Henao, Maximum Likelihood for Training Hidden Markov Principal Component Analyzers. Proceedings of the XII Symposium of Signal Processing, Images and Artificial Vision. IEEE Signal Processing Society, Colombian Chapter, September 26th-28th, 2007, Barranquilla, Colombia.
M. A. Álvarez, R. Henao and A. Orozco, Myocardial Ischemia Detection using Hidden Markov Principal Component Analysis. Proceedings of the IV Latin American Congress on Biomedical Engineering, CLAIB2007. September 24th-28th, 2007, Margarita Island, Venezuela [doi]
M. A. Álvarez and R. Henao, Hidden Markov Bayesian Principal Component Analysis, Technical Report.
M. A. Álvarez and R. Henao, Probabilistic Kernel Principal Component Analysis through Time, at ICONIP 2006 [doi]
M. A. Álvarez, R. Henao and G. Castellanos, Dimensionality Reduction of Dynamic Features using HMM and Latent Variable Observation Models (in Spanish), XI Symposium of Signal Processing, Images and Artificial Vision. IEEE Signal Processing Society, Colombian Chapter, Bogotá, Colombia, September 13-15, 2006 (Best Research Paper Award).
M. A. Álvarez, R. Henao, G. Castellanos, J.I. Godino-Llorente and A. Orozco, Kernel Principal Component Analysis through Time for Voice Disorder Classification, at EMBC 2006 [doi]
A. Orozco, M. Á. Alvarez, E. Guijarro and G. Castellanos, Identification of Spike Sources Using Proximity Analysis through Hidden Markov Models, at EMBC 2006 [doi]