Human explanation (e.g., in terms of feature importance) has been recently used to extend the communication channel between human and agent in interactive machine learning. Under this setting, human trainers provide not only the ground truth but also some form of explanation. However, this kind of human guidance was only investigated in supervised learning tasks, and it remains unclear how to best incorporate this type of human knowledge into deep reinforcement learning. In this paper, we present the first study of using human visual explanations in human-in-the-loop reinforcement learning (HRL). We focus on the task of learning from feedback, in which the human trainer not only gives binary evaluative "good" or "bad" feedback for queried state-action pairs, but also provides a visual explanation by annotating relevant features in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the model to encode task-relevant features through a context-aware data augmentation that only perturbs irrelevant features in human salient information. We choose five tasks, namely Pixel-Taxi and four Atari games, to evaluate the performance and sample efficiency of this approach. We show that our method significantly outperforms methods leveraging human explanation that are adapted from supervised learning, and Human-in-the-loop RL baselines that only utilize evaluative feedback.
Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
Michigan State University（密歇根州立大学）；CREATE for STEM（STEM创新计划）；University of Houston（休斯顿大学）
Relevance-driven Input Dropout: an Explanation-guided Regularization Technique
Fraunhofer Heinrich Hertz Institute, Department of Artificial Intelligence（弗劳恩霍夫海因里希·赫茨研究所人工智能部门）；Lehrstuhl für Angewandte Mathematik, University of Bayreuth（拜罗伊特大学应用数学研究所）；Technische Universität Berlin（柏林技术大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Centre of eXplainable Artificial Intelligence, Technological University Dublin（解释性人工智能中心，都柏林技术大学）
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models
University of Science and Technology of China（中国科学技术大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Nutritional Sciences and Wellness, University of Arizona（亚利桑那大学营养科学与健康学院）
CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Face Manipulation
Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection
Army Engineering University of PLA（中国人民解放军陆军工程大学）；National University of Defense Technology（国防科技大学）；University Of Science & Technology Beijing（北京科技大学）；National Computer Network and Information Security Management Center（国家计算机网络与信息安全管理中心）；Shanghai Jiao Tong University（上海交通大学）
K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data
Department of Informatics and Telecommunications, University of Ioannina（约阿尼纳大学信息与电信系）；Industrial Systems Institute, Athena Research Center（雅典娜研究中心工业系统研究所）；Archimedes Unit, Athena Research Center（雅典娜研究中心阿基米德单位）；Department of Computer Engineering and Informatics, University of Patras（帕特雷大学计算机工程与信息学系）；Department of Mechanical Engineering and Aeronautics, University of Patras（帕特雷大学机械工程与航空系）
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Beijing Wuzi University（北京武术大学）
A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval
School of Automation and Information Engineering, Xi’an University of Technology（西安理工大学自动化与信息工程学院）；Concordia Institute of Aerospace Design and Innovation（康考迪亚航空航天设计与创新研究所）
Do Generated Data Always Help Contrastive Learning?
Peking University（北京大学）；MIT（麻省理工学院）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；School of Mathematical Sciences, Peking University（北京大学数学科学学院）
