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Found 8 papers, 5 papers with code
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes.
Low-count Time Series Anomaly Detection
Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types.
Deep Gaussian Processes for Multi-fidelity Modeling
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models.
Entropic Trace Estimates for Log Determinants
The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others.
Bayesian Inference of Log Determinants
The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes.
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference; (ii) flexible kernels; and (iii) objective functions for hyperparameter learning alternative to the marginal likelihood.
Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty.
