Search Results - "Cutajar, Kurt"

Inherently Interpretable Time Series Classification via Multiple Instance Learning by Early, Joseph, Cheung, Gavin KC, Cutajar, Kurt, Xie, Hanting, Kandola, Jas, Twomey, Niall

“…Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this…”
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Low-count Time Series Anomaly Detection by Renz, Philipp, Cutajar, Kurt, Twomey, Niall, Cheung, Gavin K. C, Xie, Hanting

“…2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) Low-count time series describe sparse or intermittent events, which are…”
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Low-Count Time Series Anomaly Detection by Renz, Philipp, Cutajar, Kurt, Twomey, Niall, Cheung, Gavin K. C., Xie, Hanting

“…Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types…”
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Deep Gaussian Processes for Multi-fidelity Modeling by Cutajar, Kurt, Pullin, Mark, Damianou, Andreas, Lawrence, Neil, González, Javier

“…Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or…”
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Bayesian Inference of Log Determinants by Fitzsimons, Jack, Cutajar, Kurt, Osborne, Michael, Roberts, Stephen, Filippone, Maurizio

“…The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov…”
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AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models by Krauth, Karl, Bonilla, Edwin V, Cutajar, Kurt, Filippone, Maurizio

“…We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically…”
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Random Feature Expansions for Deep Gaussian Processes by Cutajar, Kurt, Bonilla, Edwin V, Michiardi, Pietro, Filippone, Maurizio

“…The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex…”
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Preconditioning Kernel Matrices by Cutajar, Kurt, Osborne, Michael A, Cunningham, John P, Filippone, Maurizio

“…The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle…”
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Entropic Trace Estimates for Log Determinants by Fitzsimons, Jack, Granziol, Diego, Cutajar, Kurt, Osborne, Michael, Filippone, Maurizio, Roberts, Stephen

“…The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal…”
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