TL;DR
Con-formal prediction can be used to enrich existing techniques, with post-hoc uncertainty quantification in a plug and play fashion, and consistently outperforms the state of the art across six out of eight real world scenarios.
Uncertainty quantification is critical in scientific machine learning, where black-box, image-based models are increasingly deployed in high-stakes settings. In many such applications, model outputs inform costly decisions, yet most methods provide only point estimates without quantifying predictive uncertainty. This c...
As artificial intelligence (AI) systems are being widely deployed in safety-critical and high-stakes applications (e.g. medical diagnosis, autonomous vehicles, financial risk assessment), there is a growing demand for providing reliable and trustworthy machine predictions. However, since AI models become more complex i...
Quantile Dynamically-Tuned Adaptive Conformal Inference (QDtACI) is proposed, a model-agnostic conformal calibration layer that constructs finite-sample intervals around any VaR forecast, using only the return series and the forecast itself, whose reliability tracks the quality of the underlying forecast.
A unified probabilistic perspective on CP and DRO is developed by viewing both as ways to turn finite calibration data into a data-dependent quantile estimator that a test score falls below with high probability.
Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem. Multiscale simulations introduce an additional challenge: robust uncertainty quantification across scales. Even within one scale, computat...
A comprehensive review and analysis of conformal prediction for LLMs is presented and a novel taxonomy that categorises existing methods into six groups is proposed, suggesting that conformal methods for LVLMs exhibit a stronger coverage-informativeness trade-off than those for LLMs.
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