Temporal Distribution Shift

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation
Apr 23, 2026Weisi Liu, Guangzeng Han, Xiaolei HuangDomain Adaptation Retrieval-Augmented Classification
University of Memphis
When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift
Apr 21, 2026Saket MagantiGraph Neural Networks Distribution Shift
Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance
Apr 19, 2026Parker Seegmiller, Sarah Masud PreumLLM Evaluation Instruction Following
Department of Computer Science Dartmouth College Hanover, NH, USA
Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
Apr 17, 2026Zhaobo Hu, Vincent Gauthier, Mehdi NaimaInvertible Neural Networks Distribution Shift Robustness
SAMOVAR, Télécom SudParis · Institut Polytechnique de Paris · CNRS – LIP6 +1
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
Apr 2, 2025Xiaobei Zou, Luolin Xiong, Kexuan Zhang +2Non-Stationary Time Series Forecasting Dynamic Graph Learning
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China · Faculty of Informatics, Università della Svizzera italiana, 69000 Lugano, Switzerland · Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy

Apr 23, 2026, cs.CLJ/K move · Enter open · S save

Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation

Weisi Liu, Guangzeng Han, Xiaolei Huang

University of Memphis

Domain Adaptation Retrieval-Augmented Classification Adaptive Retrieval Temporal Distribution Shift

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Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data where semantic distributions and domain knowledge may evolve. Unfortunately, existing studies either overlook temporal shifts or hardly capture rich shifting patterns of both semantic and knowledge. We develop Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation (KARITA) to capture diverse temporal shifts (e.g., uncertainty and feature shift), construct and integrate rich knowledge sources (e.g., medical ontology like MeSH), and leverage shifting insights for selecting-retrieval augmented learning. We evaluate KARITA on classification tasks across multiple domains, clinical, legal, and scientific corpora, demonstrating consistent improvements across multiple domains with temporal adaptation. Our results show that knowledge integration can be more critical and effective in temporal augmentation and learning.

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Latest papers 35

Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation

When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift

Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance

Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift

DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting