Machine learning traditionally assumes that the training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution can shift over time, leading to poor generalization of trained models in future time periods. This paper presents a novel prompting-based approach to temporal domain generalization that is parameter-efficient, time-efficient, and does not require access to future data during training. Our method adapts a trained model to temporal drift by learning global prompts, domain-specific prompts, and drift-aware prompts that capture underlying temporal dynamics. Experiments on classification, regression, and time series forecasting tasks demonstrate the generality of the proposed approach. The code repository will be publicly shared.
On Conditional and Compositional Language Model Differentiable Prompting
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Polytechnique Montréal（蒙特利尔理工学院）；Amazon Alexa（亚马逊Alexa）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Adapter-Enhanced Semantic Prompting for Continual Learning
Beijing Key Laboratory of Multimedia and Intelligent Software Technology（北京多媒体与智能软件技术重点实验室）；Faculty of Information Technology, Beijing University of Technology（信息科技学院，北京理工大学）；University of California at Merced（加州大学默塞德分校）；Macquarie University（麦考瑞大学）
Prompting Decision Transformer for Few-Shot Policy Generalization
Carnegie Mellon University（卡内基梅隆大学）；University of Montreal（蒙特利尔大学）；Mila（米拉）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）；UMass Amherst（马萨诸塞大学阿默斯特分校）
Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring
Southern University of Science；Technology \& Peng Cheng Laboratory；The Chinese University of Hong Kong, Shenzhen；The Hong Kong University of Science；Tsinghua University
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
McGill University（麦吉尔大学）；Microsoft Research Montreal（微软研究院蒙特利尔分部）；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）；Sony AI（索尼人工智能公司）；Google DeepMind（谷歌DeepMind）
STRAP: Spatio-Temporal Pattern Retrieval for Out-of-Distribution Generalization
City University of Hong Kong（香港城市大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））；Northeastern University（东北大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Electronic Science and Technology of China（电子科技大学）；SLAI
Fudan University（复旦大学）；Shanghai Key Laboratory of Data Science（上海市数据科学重点实验室）；School of Computer Science（计算机学院）；IFM Lab（IFM实验室）；The Chinese University of Hong Kong（香港中文大学）；Ant Group（蚂蚁集团）
