Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
arXiv:2605.12118v2 Announce Type: replace-cross Abstract: For stochastic process models, parameter inference is often severely bottlenecked by computationally expensive likelihood functions. Simulation-based inference (SBI) bypasses this restriction by constructing amortized surrogate likelihoods, but most SBI methods assume a black-box data generating process. While these surrogates are exact in the limit of infinite training data, practical scenarios force a strict tradeoff between model quality and simulation cost. In this work, we loosen the black-box assumption of SBI to improve this trad
This research addresses fundamental computational bottlenecks in AI model training, a critical area as model complexity and data requirements continue to grow.
Improved efficiency in AI training will accelerate research, lower compute costs, and enable more sophisticated models across various applications, impacting R&D and deployment strategies.
The proposed score-augmented loss functions offer a more efficient method for constructing accurate surrogate likelihoods in simulation-based inference, reducing the computational burden of complex AI model training.
- · AI research labs
- · Cloud computing providers (reduced egress costs for users)
- · Industries relying on complex simulations (e.g., drug discovery, climate modelin
- · Legacy simulation-only software providers
- · Organizations with inefficient AI training infrastructure
More complex and accurate AI models can be developed and deployed faster.
This could democratize access to advanced AI capabilities by lowering the compute barrier for development.
Accelerated AI development might lead to breakthroughs in fields previously limited by computational constraints, such as materials science or personalized medicine.
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Read at arXiv cs.LG