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uncertainty calibration

topic3 events
papersTODAY 04:00 UTC

Bayesian Optimisation Method Combines Expert Gaussian Processes With Calibrated Uncertainty

A new arXiv preprint proposes using a product-of-experts Gaussian process as the surrogate model in Bayesian optimisation, rather than a single global GP. The authors address the cubic scaling cost of standard GP regression with training set size, which restricts its use on larger datasets, and add a calibration step for uncertainty estimates. The work falls in the machine learning methodology category and has not yet been peer reviewed.

bayesian-optimisation/gaussian-processes/uncertainty/arxiv
papersSEP 12 04:00 UTC

Calibration-Aware Uncertainty Cascades for Heterogeneous Model Collaboration

A new arXiv preprint proposes a routing method for combining multiple models that uses calibration-aware uncertainty estimates to decide when to escalate a query to a larger, more expensive model. The approach aims to avoid the rigidity of trained routers, which are tied to fixed cost or accuracy trade-offs, while still balancing predictive quality against inference cost. The work targets heterogeneous model collaboration settings where different models offer complementary strengths.

research/model-routing/uncertainty/inference-cost