In this paper, we revisit Stochastic Continuous Submodular Maximization in
both offline and online settings, which can benefit wide applications in
machine learning and operations research areas. We present a boosting framework
covering gradient ascent and online gradient ascent. The fundamental ingredient
of our methods is a novel non-oblivious function $F$ derived from a
factor-revealing optimization problem, whose any stationary point provides a
$(1-e^{-\gamma})$-approximation to the global maximum of the $\gamma$-weakly
DR-submodular objective function $f\in C^{1,1}_L(\mathcal{X})$. Under the
offline scenario, we propose a boosting gradient ascent method achieving
$(1-e^{-\gamma}-\epsilon^{2})$-approximation after $O(1/\epsilon^2)$
iterations, which improves the $(\frac{\gamma^2}{1+\gamma^2})$ approximation
ratio of the classical gradient ascent algorithm. In the online setting, for
the first time we consider the adversarial delays for stochastic gradient
feedback, under which we propose a boosting online gradient algorithm with the
same non-oblivious function $F$. Meanwhile, we verify that this boosting online
algorithm achieves a regret of $O(\sqrt{D})$ against a
$(1-e^{-\gamma})$-approximation to the best feasible solution in hindsight,
where $D$ is the sum of delays of gradient feedback. To the best of our
knowledge, this is the first result to obtain $O(\sqrt{T})$ regret against a
$(1-e^{-\gamma})$-approximation with $O(1)$ gradient inquiry at each time step,
when no delay exists, i.e., $D=T$. Finally, numerical experiments demonstrate
the effectiveness of our boosting methods.

本文研究了随机连续子模最大化问题，在离线和在线两种场景下提出了一种基于 boosting 方法的算法，有效地解决了 DR-submodular 目标函数的最大化问题，并在对抗性环境下讨论了梯度反馈的在线情况。数值实验验证了算法的有效性。

随机连续次模最大化：通过非遗忘函数增强

Stochastic Continuous Submodular Maximization: Boosting via  Non-oblivious Function

This paper presents a novel approach for lecture video indexing using a
boosted deep convolutional neural network system. The indexing is performed by
matching high quality slide images, for which text is either known or
extracted, to lower resolution video frames with possible noise, perspective
distortion, and occlusions. We propose a deep neural network integrated with a
boosting framework composed of two sub-networks targeting feature extraction
and similarity determination to perform the matching. The trained network is
given as input a pair of slide image and a candidate video frame image and
produces the similarity between them. A boosting framework is integrated into
our proposed network during the training process. Experimental results show
that the proposed approach is much more capable of handling occlusion, spatial
transformations, and other types of noises when compared with known approaches.

本篇论文提出一种使用深度卷积神经网络和增强学习框架进行讲座视频索引的新方法，该方法可以有效地处理遮挡、空间变换和其他类型的噪声。

使用增强边缘最大化神经网络进行讲座视频索引

Lecture video indexing using boosted margin maximizing neural networks

Collaborative filtering (CF) aims to predict users' ratings on items
according to historical user-item preference data. In many real-world
applications, preference data are usually sparse, which would make models
overfit and fail to give accurate predictions. Recently, several research works
show that by transferring knowledge from some manually selected source domains,
the data sparseness problem could be mitigated. However for most cases, parts
of source domain data are not consistent with the observations in the target
domain, which may misguide the target domain model building. In this paper, we
propose a novel criterion based on empirical prediction error and its variance
to better capture the consistency across domains in CF settings. Consequently,
we embed this criterion into a boosting framework to perform selective
knowledge transfer. Comparing to several state-of-the-art methods, we show that
our proposed selective transfer learning framework can significantly improve
the accuracy of rating prediction tasks on several real-world recommendation
tasks.

该研究论文提出一种基于经验预测误差和方差的新标准，用于更好地捕捉协同过滤设置中跨领域的一致性，从而在提高评分预测精度方面显著改进了几项最先进的方法。