Generative Conformal Prediction with Vectorized Non-Conformity Scores
This work addresses a limitation in uncertainty quantification for machine learning models, offering improved flexibility and efficiency, though it is incremental as it builds on existing conformal prediction methods.
The paper tackles the problem of overly conservative uncertainty sets in conformal prediction for multi-dimensional settings by proposing a generative framework with vectorized non-conformity scores, resulting in more precise uncertainty allocation and outperforming state-of-the-art methods in experiments.
Conformal prediction (CP) provides model-agnostic uncertainty quantification with guaranteed coverage, but conventional methods often produce overly conservative uncertainty sets, especially in multi-dimensional settings. This limitation arises from simplistic non-conformity scores that rely solely on prediction error, failing to capture the prediction error distribution's complexity. To address this, we propose a generative conformal prediction framework with vectorized non-conformity scores, leveraging a generative model to sample multiple predictions from the fitted data distribution. By computing non-conformity scores across these samples and estimating empirical quantiles at different density levels, we construct adaptive uncertainty sets using density-ranked uncertainty balls. This approach enables more precise uncertainty allocation -- yielding larger prediction sets in high-confidence regions and smaller or excluded sets in low-confidence regions -- enhancing both flexibility and efficiency. We establish theoretical guarantees for statistical validity and demonstrate through extensive numerical experiments that our method outperforms state-of-the-art techniques on synthetic and real-world datasets.