Recent work in vision-and-language demonstrates that large-scale pretraining
can learn generalizable models that are efficiently transferable to downstream
tasks. While this may improve dataset-scale aggregate metrics, analyzing
performance around hand-crafted subgroups targeting specific bias dimensions
reveals systemic undesirable behaviors. However, this subgroup analysis is
frequently stalled by annotation efforts, which require extensive time and
resources to collect the necessary data. Prior art attempts to automatically
discover subgroups to circumvent these constraints but typically leverages
model behavior on existing task-specific annotations and rapidly degrades on
more complex inputs beyond "tabular" data, none of which study
vision-and-language models. This paper presents VLSlice, an interactive system
enabling user-guided discovery of coherent representation-level subgroups with
consistent visiolinguistic behavior, denoted as vision-and-language slices,
from unlabeled image sets. We show that VLSlice enables users to quickly
generate diverse high-coherency slices in a user study (n=22) and release the
tool publicly.

这篇论文介绍了 VLSlice，一个交互式系统，使用户能够引导性地发现与一致的视觉语言行为相关的连贯表征层次的子群体，称为视觉语言切片，从无标签的图像集中。我们展示了 VLSlice 在用户研究中（n=22）能够快速生成多样高一致性的切片，并公开发布该工具。

VLSlice: 交互式视觉与语言切片发现

VLSlice: Interactive Vision-and-Language Slice Discovery

Subgroup analysis of treatment effects plays an important role in
applications from medicine to public policy to recommender systems. It allows
physicians (for example) to identify groups of patients for whom a given drug
or treatment is likely to be effective and groups of patients for which it is
not. Most of the current methods of subgroup analysis begin with a particular
algorithm for estimating individualized treatment effects (ITE) and identify
subgroups by maximizing the difference across subgroups of the average
treatment effect in each subgroup. These approaches have several weaknesses:
they rely on a particular algorithm for estimating ITE, they ignore
(in)homogeneity within identified subgroups, and they do not produce good
confidence estimates. This paper develops a new method for subgroup analysis,
R2P, that addresses all these weaknesses. R2P uses an arbitrary, exogenously
prescribed algorithm for estimating ITE and quantifies the uncertainty of the
ITE estimation, using a construction that is more robust than other methods.
Experiments using synthetic and semi-synthetic datasets (based on real data)
demonstrate that R2P constructs partitions that are simultaneously more
homogeneous within groups and more heterogeneous across groups than the
partitions produced by other methods. Moreover, because R2P can employ any ITE
estimator, it also produces much narrower confidence intervals with a
prescribed coverage guarantee than other methods.

本文介绍了 R2P 方法，它使用任意外生估计算法来估计个体化治疗效果，并量化治疗效果估计的不确定性，通过更强大的构造实现更均匀的缺陷组和更异构的子组划分，同时产生比其他方法更窄的置信区间。

异质性治疗效果的强健递归划分及不确定性量化

Robust Recursive Partitioning for Heterogeneous Treatment Effects with  Uncertainty Quantification

An important step in developing individualized treatment strategies is to
correctly identify subgroups of a heterogeneous population, so that specific
treatment can be given to each subgroup. In this paper, we consider the
situation with samples drawn from a population consisting of subgroups with
different means, along with certain covariates. We propose a penalized approach
for subgroup analysis based on a regression model, in which heterogeneity is
driven by unobserved latent factors and thus can be represented by using
subject-specific intercepts. We apply concave penalty functions to pairwise
differences of the intercepts. This procedure automatically divides the
observations into subgroups. We develop an alternating direction method of
multipliers algorithm with concave penalties to implement the proposed approach
and demonstrate its convergence. We also establish the theoretical properties
of our proposed estimator and determine the order requirement of the minimal
difference of signals between groups in order to recover them. These results
provide a sound basis for making statistical inference in subgroup analysis.
Our proposed method is further illustrated by simulation studies and analysis
of the Cleveland heart disease dataset.

该论文提出了一种基于回归模型的受惩罚子组分析方法，该方法利用惩罚来划分不同子组，通过对被观察截距的差异进行配对惩罚来刻画异质性，并且该方法经过了统计检验的验证。