1 citations · 2 across the 13 of their papers we have counts for
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Uncertainty quantification via conformal prediction in data assimilation
Catherine George, Alireza Javanmardi, Tijana Janjić +1
Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this study, we investigate the appl…
Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score
Stefan Haas, Luca Killmaier, Alireza Javanmardi +1
Ordinal classification (OC) arises in high-stakes domains such as medicine and finance, where uncertainty quantification must account for the severity of ordinal errors. Conformal…
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…
Conformal Prediction with Partially Labeled Data
Alireza Javanmardi, Yusuf Sale, Paul Hofman +1
While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise. In the setting of superset learning or…
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization
Navid Ansari, Alireza Javanmardi, Eyke Hüllermeier +2
Bayesian optimization (BO) provides a powerful framework for optimizing black-box, expensive-to-evaluate functions. It is therefore an attractive tool for engineering design proble…
Conformal Prediction Intervals for Remaining Useful Lifetime Estimation
Alireza Javanmardi, Eyke Hüllermeier
The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in worki…