Abstract
We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the computational cost of inference and preserving some or all of the reliability guarantees of parametric methods. The first approach is broadly applicable, while the second is tailored to a class of particle physics analyses that admit a semi-parametric NSBI formulation. With only a modest compromise in raw sensitivity, these methods represent an important step toward computationally efficient NSBI in offline analyses and also open the door to the exploration of trigger-level applications in the future. Based on our comparison studies, we recommend the use of our first approach, Latent Categories, for robust and efficient inference.
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Cite this article
APA 7
Benevedes, S., Dehghan, M., Ghosh, A., & Park, T. H. (2026). Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios. https://omanscience.com/en/articles/hybrid-neural-simulation-based-inference-for-robust-applications-and-limited-budget-scenarios
MLA 9
Benevedes, Sean, et al. "Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios." https://omanscience.com/en/articles/hybrid-neural-simulation-based-inference-for-robust-applications-and-limited-budget-scenarios.
Chicago (author–date)
Benevedes, Sean, Mani Dehghan, Aishik Ghosh, and Tae Hyoun Park. 2026. "Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios." https://omanscience.com/en/articles/hybrid-neural-simulation-based-inference-for-robust-applications-and-limited-budget-scenarios.
Harvard
Benevedes, S., Dehghan, M., Ghosh, A. and Park, T. H. (2026) 'Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios', Available at: https://omanscience.com/en/articles/hybrid-neural-simulation-based-inference-for-robust-applications-and-limited-budget-scenarios.
Vancouver
Benevedes S, Dehghan M, Ghosh A, Park TH. Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios. https://omanscience.com/en/articles/hybrid-neural-simulation-based-inference-for-robust-applications-and-limited-budget-scenarios
IEEE
S. Benevedes, M. Dehghan, A. Ghosh, and T. H. Park, "Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios," https://omanscience.com/en/articles/hybrid-neural-simulation-based-inference-for-robust-applications-and-limited-budget-scenarios.