Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time. The goal of online learning is to ensure that the online learner would make a sequence of accurate predictions (or correct decisions) given the knowledge of correct answers to previous prediction or learning tasks and possibly additional information. This is in contrast to many traditional batch learning or offline machine learning algorithms that are often designed to train a model in batch from a given collection of training data instances. This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the learning type and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) supervised online learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) unsupervised online learning where there is no feedback available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.
A Survey on Distributed Online Optimization and Game
Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Nanyang Technological University（南洋理工大学）；Harvard University（哈佛大学）
A Comprehensive Study on Temporal Modeling for Online Action Detection
University of Electronic Science and Technology of China（电子科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages
Bennett University（班尼特大学）；Techno Main Salt Lake（泰克诺主盐湖校区）；Indian Institute of Information Technology & Management Gwalior（瓜利尔印度信息技术与管理学院）；Indian Institute of Engineering Science and Technology, Shibpur（希布普尔印度工程科学与技术学院）；National Institute of Technology, Silchar（锡尔恰尔国家理工学院）
A Comprehensive Survey on Aspect Based Sentiment Analysis
Vellore Institute Of Technology（韦洛尔理工大学）；School of Computer Science and Engineering- SCOPE（计算机科学与工程学院-SCOPE）
基于方面的情感分析（Aspect Based Sentiment Analysis，ABSA）是自然语言处理的一个分支，它主要是将数据分成方面，并最终提取情感信息。已知ABSA比一般的情感分析提供更多关于上下文的信息。在这项研究中，我们的目的是探索各种方法，同时执行ABSA，并提供一个比较研究。这份调查报告深入讨论了各种解决方案，并对它们进行了比较。并方便地分为几个部分，以获得对该过程的整体看法。
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
Concordia University（康考迪亚大学）；Aqaba University of Technology（亚喀巴科技大学）；Khalifa University（哈利法大学）；University of Alberta（阿尔伯塔大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Istinye University（伊斯蒂涅大学）；University of Waterloo（滑铁卢大学）
Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques
Qingdao University of Science and Technology（青岛科技大学）
A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning
Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Ocean University of China（中国海洋大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Anhui University（安徽大学）；The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）
