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Direct Inference on Admissible Sets (Diona)

Sabug, Lorenzo, Jr., Kerrigan Eric · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31526 · cited by 0 works (OpenAlex) · open-access PDF

Hindi

हम लेटेन डेटा रिगर्शन विधि का प्रस्ताव करते हैं, जिसे लेटेन डेटा रिगर्शन विधि कहा जाता है Direct Inference on Admissible Sets (Diona) जो प्रत्यक्ष रूप से नमूने का उपयोग करके एक पूछताछ बिंदु पर राज्य और इसके उत्प्रेरकों का अनुमान लगाने के लिए, इसलिए उनके स्वीकार्य सीमाओं के साथ, लंबे वैश्विक प्रशिक्षण के बिना। पूछताछ पर मूल्यों और उत्प्रेरकों को फेंकने और घने नमूने के तहत पड़ोसी नमूने को लैटिन परिवर्तों के रूप में, एक सीमित अनुकूलन समस्या में, लंबे प्रशिक्षण लूपों का बोझ तीन से आठ आयामों के छह कार्यों पर गैर-लाइनर रिगर्शन बेंचमार्क में, Diona को अंतिम नम

Machine translation (Meta M2M100); the English original follows.

Abstract

We propose a new regression method named Direct Inference on Admissible Sets (Diona) that directly uses samples to estimate the state and its derivatives at a query point, along with their admissible bounds, without long global training. By casting the values and derivatives at the query and neighbouring samples as latent variables in a constrained optimisation problem, the burden of long training loops becomes one of on-demand local solves. We establish Diona's theoretical properties including finite-sample coverage and, in the noiseless case, convergence to the true state and derivatives under dense sampling. In a nonlinear regression benchmark on six functions of three to eight dimensions, Diona was more accurate than a Gaussian process (GP) on noiseless data especially with denser data sets. Furthermore, as with GP, it produced uncertainty bounds that contained the true value at more than 95% of query points in four functions. Diona's time to a first prediction after a data set update was three to four orders of magnitude shorter than that of the GP, and two to three orders shorter than that of a neural network, which was more accurate but offers no uncertainty quantification. This low latency is paid for by on-demand predictions, so this latency advantage applies when relatively few predictions are needed between data updates.

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Topics: Inference, Mathematics, Algorithm, Benchmark (surveying), Regression, Gaussian process

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