A vast amount of audio-visual data is available on the Internet thanks to
video streaming services, to which users upload their content. However, there
are difficulties in exploiting available data for supervised statistical models
due to the lack of labels. Unfortunately, generating labels for such amount of
data through human annotation can be expensive, time-consuming and prone to
annotation errors. In this paper, we propose a method to automatically extract
entity-video frame pairs from a collection of instruction videos by using
speech transcriptions and videos. We conduct experiments on image recognition
and visual grounding tasks on the automatically constructed entity-video frame
dataset of How2. The models will be evaluated on new manually annotated portion
of How2 dev5 and val set and on the Flickr30k dataset. This work constitutes a
first step towards meta-algorithms capable of automatically construct
task-specific training sets.

本文提出一种自动从指示视频中提取实体 - 视频帧对的方法，以用于创建任务特定的训练集元算法。

用文字转录结果定位物体检测

Grounding Object Detections With Transcriptions

There is growing interest in estimating and analyzing heterogeneous treatment
effects in experimental and observational studies. We describe a number of
meta-algorithms that can take advantage of any supervised learning or
regression method in machine learning and statistics to estimate the
Conditional Average Treatment Effect (CATE) function. Meta-algorithms build on
base algorithms---such as Random Forests (RF), Bayesian Additive Regression
Trees (BART) or neural networks---to estimate the CATE, a function that the
base algorithms are not designed to estimate directly. We introduce a new
meta-algorithm, the X-learner, that is provably efficient when the number of
units in one treatment group is much larger than in the other, and can exploit
structural properties of the CATE function. For example, if the CATE function
is linear and the response functions in treatment and control are Lipschitz
continuous, the X-learner can still achieve the parametric rate under
regularity conditions. We then introduce versions of the X-learner that use RF
and BART as base learners. In extensive simulation studies, the X-learner
performs favorably, although none of the meta-learners is uniformly the best.
In two persuasion field experiments from political science, we demonstrate how
our new X-learner can be used to target treatment regimes and to shed light on
underlying mechanisms. A software package is provided that implements our
methods.

介绍了一种利用机器学习和统计学中的任何监督式学习或回归方法来估计条件平均处理效应（CATE）函数的元算法，其中包括一种新的元学习者 X-learner，通过广泛的模拟研究和两个政治学实验表明该算法的有效性和应用。

使用机器学习估计异质性治疗效果的元学习器

Meta-learners for Estimating Heterogeneous Treatment Effects using  Machine Learning

Online learning with delayed feedback has received increasing attention
recently due to its several applications in distributed, web-based learning
problems. In this paper we provide a systematic study of the topic, and analyze
the effect of delay on the regret of online learning algorithms. Somewhat
surprisingly, it turns out that delay increases the regret in a multiplicative
way in adversarial problems, and in an additive way in stochastic problems. We
give meta-algorithms that transform, in a black-box fashion, algorithms
developed for the non-delayed case into ones that can handle the presence of
delays in the feedback loop. Modifications of the well-known UCB algorithm are
also developed for the bandit problem with delayed feedback, with the advantage
over the meta-algorithms that they can be implemented with lower complexity.

本文系统研究了在线学习与延迟反馈的主题，分析了延迟对在线学习算法的遗憾值的影响，提出了黑盒元算法以及基于 UCB 算法的改进方法来解决带有延迟反馈的学习问题。