Full Professor, University of California, Berkeley
4 papers at NeurIPS 2025
When multiple conformal predictors are available, we allow data-dependent selection without the loss of coverage.
We introduce Backward Conformal Prediction, a new method that adapts coverage levels to enforce interpretable, data-dependent prediction set sizes, with provable guarantees and practical estimators.
