Direct Inference on Admissible Sets (Diona)
Fast predictions with built-in confidence bounds, no lengthy retraining required
A new method called Diona estimates values and their uncertainty ranges from data samples almost instantly—without the slow training loops that neural networks and other methods require. On noiseless datasets, it outperformed Gaussian processes (a standard tool for uncertainty quantification) and produced reliable confidence bounds, while being thousands of times faster to deliver a first prediction after new data arrives.
Real-time systems that need both speed and confidence intervals—like robotics, control systems, or sensor networks—face a hard choice between accurate methods that require expensive retraining and fast methods that don't quantify uncertainty. Diona removes this tradeoff when predictions are needed sporadically: it delivers answers in milliseconds rather than minutes or hours, making it practical for applications where data updates happen frequently but predictions happen rarely.