We consider the problem of distribution-free predictive inference, with the
goal of producing predictive coverage guarantees that hold conditionally rather
than marginally. Existing methods such as conformal prediction offer marginal
coverage guarantees, where predictive coverage holds on average over all
possible test points, but this is not sufficient for many practical
applications where we would like to know that our predictions are valid for a
given individual, not merely on average over a population. On the other hand,
exact conditional inference guarantees are known to be impossible without
imposing assumptions on the underlying distribution. In this work we aim to
explore the space in between these two, and examine what types of relaxations
of the conditional coverage property would alleviate some of the practical
concerns with marginal coverage guarantees while still being possible to
achieve in a distribution-free setting.

本文考虑面向分布无关的预测推断问题，目标是生成有条件而非边缘的预测覆盖保证。我们旨在探索边缘覆盖保证的实际问题，并研究一些能够缓解一些实际关注的条件覆盖性质松弛类型，同时仍能在分布无关的环境下实现。