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Found 3 papers, 1 papers with code
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates
First, we notice that these models are trained on uniformly distributed inputs, which impairs predictive accuracy on non-uniform data - a setting arising from any typical BO loop due to exploration-exploitation trade-offs.
Efficient Semi-Implicit Variational Inference
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI).
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