Conformal prediction (CP) enables machine learning models to output
prediction sets with guaranteed coverage rate, assuming exchangeable data.
Unfortunately, the exchangeability assumption is frequently violated due to
distribution shifts in practice, and the challenge is often compounded by the
lack of ground truth labels at test time. Focusing on classification in this
paper, our goal is to improve the quality of CP-generated prediction sets using
only unlabeled data from the test domain. This is achieved by two new methods
called ECP and EACP, that adjust the score function in CP according to the base
model's uncertainty on the unlabeled test data. Through extensive experiments
on a number of large-scale datasets and neural network architectures, we show
that our methods provide consistent improvement over existing baselines and
nearly match the performance of supervised algorithms.

通过两种新方法 ECP 和 EACP，根据基模型在未标记测试数据上的不确定性调整 CP 中的评分函数，从而仅使用测试域中的未标记数据改进 CP 生成的预测集的质量。通过对许多大规模数据集和神经网络架构进行广泛实验，我们展示了我们的方法相对于现有基准算法的持续改进，并几乎与监督算法的性能相匹配。

无标签条件下适应分布偏移的适应性符合性预测

Adapting Conformal Prediction to Distribution Shifts Without Labels

We study causal effect estimation in a setting where the data are not i.i.d.
(independent and identically distributed). We focus on exchangeable data
satisfying an assumption of independent causal mechanisms. Traditional causal
effect estimation frameworks, e.g., relying on structural causal models and
do-calculus, are typically limited to i.i.d. data and do not extend to more
general exchangeable generative processes, which naturally arise in
multi-environment data. To address this gap, we develop a generalized framework
for exchangeable data and introduce a truncated factorization formula that
facilitates both the identification and estimation of causal effects in our
setting. To illustrate potential applications, we introduce a causal P\'olya
urn model and demonstrate how intervention propagates effects in exchangeable
data settings. Finally, we develop an algorithm that performs simultaneous
causal discovery and effect estimation given multi-environment data.

在非独立同分布数据的情景中，我们研究因果效应的估计，重点关注满足独立因果机制假设的可交换数据。为了弥补现有因果效应估计框架的不足，我们开发了针对可交换数据的广义框架，并引入了截断因式分解公式，以促进在我们的情景中对因果效应的识别和估计。为了说明潜在的应用，我们介绍了一个因果泊利亚球模型，并展示了干预在可交换数据设置中如何传播效应。最后，我们开发了一个算法，可以在多环境数据中进行同时因果发现和效应估计。

Do Finetti：对可交换数据的因果效应研究

Do Finetti: On Causal Effects for Exchangeable Data

Recently there has been growing interest in modeling sets with
exchangeability such as point clouds. A shortcoming of current approaches is
that they restrict the cardinality of the sets considered or can only express
limited forms of distribution over unobserved data. To overcome these
limitations, we introduce Energy-Based Processes (EBPs), which extend energy
based models to exchangeable data while allowing neural network
parameterizations of the energy function. A key advantage of these models is
the ability to express more flexible distributions over sets without
restricting their cardinality. We develop an efficient training procedure for
EBPs that demonstrates state-of-the-art performance on a variety of tasks such
as point cloud generation, classification, denoising, and image completion.

本文介绍了一种名为 Energy-Based Processes（EBP）的方法，它能够扩展能量模型以处理可交换的数据，并允许使用神经网络的参数化能量函数。该方法能够灵活表达集合上的分布，且具有最先进的性能。