Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Abstract Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global …
Zoom In Disparities in Healthcare LLM Q&A
Abstract Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the …
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Multiclass Local Calibration with the Jensen-Shannon Distance
Abstract Developing trustworthy Machine Learning (ML) models requires their predicted probabilities to be well-calibrated, meaning they should reflect true-class frequencies. Among …
To Ask or Not to Ask: Learning to Require Human Feedback
Abstract Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), …
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