Academic staff
Researcher interests: Probabilistic Graphical Models. Fundamental representations and methods for inference and learning in large scale domains, with an emphasis on high-level elements such as structure learning, the discovery of hidden variables and classes, transfer of knowledge between related classes/tasks. I have recently taken a particular interest to nonlinear high-dimensional representation of continuous or hybrid distributions. Real-life Applications. Applying fundamental techniques to challenging domains such as computational biology and machine vision. Recently, my I have started focusing on the development of principled techniques based on probabilistic knowledge for diagnosis in the field of medical informatics.
FACULTY / SCHOOL: Faculty of Social Sciences
DEPARTMENT: Statistics
Selected Publications
globally optimal learning for structured elliptical losses (2019)|
NeurIPS 2019 : Thirty-third Conference on Neural Information Processing Systems|
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learning rules first classifiers (2019)|
arXiv: Learning|
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learning rules first classifiers (2019)|
The 22nd International Conference on Artificial Intelligence and Statistics|
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spectral algorithm for low rank multitask regression (2019)|
arXiv preprint arXiv:1910.12204|
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unmixing k gaussians with application to hyperspectral imaging (2019)|
IEEE Transactions on Geoscience and Remote Sensing|
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learning with rules (2018)|
arXiv preprint arXiv:1803.03155|
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ml for flood forecasting at scale (2018)|
arXiv preprint arXiv:1901.09583|
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towards global remote discharge estimation using the few to estimate the many (2018)|
arXiv preprint arXiv:1901.00786|
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logistic markov decision processes (2017)|
Twenty-Sixth International Joint Conference on Artificial Intelligence|
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signal detection in complex structured para normal noise (2017)|
IEEE Transactions on Signal Processing|
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