We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification
School of Software, Northeastern University（东北大学软件学院）；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）；College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；School of Software, University of Science and Technology of China（中国科学技术大学软件学院）
Zhipeng Liu, Peibo Duan, Xuan Tang, Haodong Jing, Mingyang Geng, Yongsheng Huang, Jialu Xu, Bin Zhang, Binwu Wang
备注：
This paper has been accepted for publication at ACM WWW 2026
The World Wide Web thrives on intelligent services that rely on accurate time series classification, which has recently witnessed significant progress driven by advances in deep learning. However, existing studies face challenges in domain incremental learning. In this paper, we propose a lightweight and robust dual-causal disentanglement framework (DualCD) to enhance the robustness of models under domain incremental scenarios, which can be seamlessly integrated into time series classification models. Specifically, DualCD first introduces a temporal feature disentanglement module to capture class-causal features and spurious features. The causal features can offer sufficient predictive power to support the classifier in domain incremental learning settings. To accurately capture these causal features, we further design a dual-causal intervention mechanism to eliminate the influence of both intra-class and inter-class confounding features. This mechanism constructs variant samples by combining the current class's causal features with intra-class spurious features and with causal features from other classes. The causal intervention loss encourages the model to accurately predict the labels of these variant samples based solely on the causal features. Extensive experiments on multiple datasets and models demonstrate that DualCD effectively improves performance in domain incremental scenarios. We summarize our rich experiments into a comprehensive benchmark to facilitate research in domain incremental time series classification.
Ant Group（蚂蚁集团）；University of Georgia（佐治亚大学）；University of Virginia（弗吉尼亚大学）
The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public policy, marketing campaigns, and so on, which facilitates the development of causal effect estimation. Although significant advances have been made to overcome the challenges in the academic area, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data, which is unrealistic in most industrial applications. In this paper, we investigate a new ind…
Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；Institute for Information Sciences, University of Kansas（信息科学研究所，堪萨斯大学）
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Innovation Lab of CETC（中国电子科技集团公司创新实验室）
Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based recommender models, which are state-of-the-art techniques for collaborative recommendation. To pursue high efficiency, we set the target as using only new data for model updating, meanwhile not sacrificing the recommendation accuracy compared with full model retraining. This is non-trivial to achieve, since the interaction data participates in both the graph structure for model construction and the loss function for …
MIT Economics（麻省理工学院经济学系）；Gatsby Unit, UCL（伦敦大学学院盖茨比研究组）
We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves. Treatment and covariates may be discrete or continuous in general spaces. Due to a decomposition property specific to the RKHS, our estimators have simple closed form solutions. We prove uniform consistency with finite sample rates via original analysis of generalized kernel ridge regression. We extend our main results to counterfactual distributions and to causal functions identified by front and back door criteria. We achieve state-of-the-art…
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education（教育部可持续城市智能交通工程研究中心）；National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University（西南交通大学智能交通大数据应用技术国家工程实验室）；Sichuan International Travel Healthcare Center, Chengdu Customs（四川国际旅行卫生中心，成都海关）；Endocrinology Department, Hospital of Chengdu University of Traditional Chinese Medicine（成都中医药大学内分泌科）
University of Tübingen（蒂宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations to empower beneficiaries to achieve a more favorable outcome. To ensure an effective remedy, suggested interventions must not only be low-cost but also robust and fair. This goal is accomplished by providing similar explanations to individuals who are alike. This study explores the concept of individual fairness and adversarial robustness in causal algorithmic recourse and addresses the challenge of achieving both. …
The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categories. Many works have been proposed to alleviate this phenomenon, whereas most of them either fall into the stability-plasticity dilemma or take too much computation or storage overhead. Inspired by the gradient boosting algorithm to gradually fit the residuals between the target model and the previous ensemble model, we propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories ad…
This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at arbitrary times, with the objective of improving data-efficiency and interpretability of model-based reinforcement learning (RL) techniques. The proposed algorithm initially predicts observations with the Markov assumption, and incrementally introduces new hidden variables to explain and reduce the stochasticity of the observations. The hidden variables are memory units that keep track of pertinent past events. Such events are systematically identified by their informa…
TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting
Software College, Northeastern University, Shenyang, China（东北大学软件学院）；School of Software, University of Science and Technology of China（中国科学技术大学软件学院）；Xichang Satellite Launch Center, Xichang, China（西昌卫星发射中心）
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；China Communications Technology Information Group Co., Ltd.（中国交通通信信息集团有限公司）；Tsinghua University（清华大学）
The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: the pre-trained graph representation learning model tested with full graphs underperforms the model tested with well-pruned graphs. This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence. To tackle this issue, we propose Robust Causal Graph Representation Lear…
SRI International（斯坦福国际研究院）；University of Central Florida（中佛罗里达大学）；University of Wisconsin（威斯康星大学）；U.S. Army Research Laboratory（美国陆军研究实验室）
Attribution methods have been developed to explain the decision of a machine learning model on a given input. We use the Integrated Gradient method for finding attributions to define the causal neighborhood of an input by incrementally masking high attribution features. We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood. Our study indicates that benign inputs are robust to the masking of high attribution features but adversarial inputs generated by the state-of-the-art adversarial attack methods such as DeepFool, FGSM, CW and PGD, are not r…
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
The Hong Kong Polytechnic University（香港理工大学）；Sichuan University（四川大学）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong Baptist University（香港浸会大学）；NEOMA Business School（诺欧商学院）
