Reliable decision-making relies on predictive sets that capture the true outcome with a specified probability. In this talk, I will explore conformal prediction, a statistical approach that delivers rigorous finite-sample guarantees. While standard conformal methods provide valid marginal coverage, they do not ensure coverage conditional on specific inputs. I will present a framework to quantify conditional miscoverage and discuss strategies to improve conditional reliability, including the tradeoff between set size and conditional coverage.
We will explore how conformal based strategies can be applied to estimate highest density regions, bypassing density estimation.
The talk will highlight both theoretical insights and practical implications for building trustworthy predictive systems.