Direct Inference on Admissible Sets (Diona)
Read the original on arXiv Machine Learning →Direct Inference on Admissible Sets (Diona) is a regression method that estimates a state and its derivatives at a query point directly from samples, providing admissible bounds without extensive global training. It formulates the estimation as a constrained optimisation problem over latent variables, enabling on‑demand local solves. In benchmarks, Diona outperformed Gaussian processes on noiseless data, offered uncertainty bounds covering the true value in most cases, and achieved first predictions up to four orders of magnitude faster than Gaussian processes and two to three orders faster than neural networks.
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