Daniel Augusto
Hi! I'm a Ph.D. student from UCL at the SML group. My research in machine learning is focused on deep Gaussian processes for geospatial data. I'm particularly interested in the interaction of applied expert knowledge with prior specification and how approximate inference methods can change this interpretability.
This webpage contains my contact information and a list of publications. If you prefer a printed copy of my resumé, here is a link to its location.
Bibliography
2023
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Actually Sparse Variational Gaussian Processes.
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In: 26th International Conference on Artificial Intelligence and Statistics (AISTATS) -
Thin and Deep Gaussian Processes.
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In: Advances in Neural Information Processing Systems (NeurIPS) 36
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2022
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Parallel MCMC Without Embarrassing Failures.
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In: 25th International Conference on Artificial Intelligence and Statistics (AISTATS) -
Deep Mahalanobis Gaussian Process.
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In: NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
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2021
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Learning GPLVM with arbitrary kernels using the unscented transformation.
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In: 24th International Conference on Artificial Intelligence and Statistics (AISTATS)
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2019
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No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization.
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In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) -
Evaluation of Data Based Normal Behavior Models for Fault Detection in Wind Turbines.
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In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS)
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Contributions on latent projections for Gaussian process modeling.
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M.Sc. thesis. Jointly supervised by: João Paulo Gomes, César Lincoln C. Mattos. Universidade Federal do Ceará