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Found 3 papers, 1 papers with code
Dynamic Online Ensembles of Basis Expansions
Practical Bayesian learning often requires (1) online inference, (2) dynamic models, and (3) ensembling over multiple different models.
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling
In this paper, we operate within the Bayesian paradigm, relying on Gaussian processes as our models.
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery.
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