Temporal Domain Adaptation

Latest papers

10 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Conditional Flow Matching for Transport Between Markov Processes
Oct 5, 2026Syamantak Kumar, Dheeraj Nagaraj, Saptarshi Roy +1Conditional Flow Matching Domain Adaptation
The University of Texas at Austin, Department of Computer Science · Google Deepmind · Mohamed bin Zayed University of Artificial Intelligence, CMS Division +1
Multi-Scale Temporal Domain Alignment for Federated Video Domain Adaptation
Aug 29, 2026Lee En-Yi Hannah, Haozhi Cao, Yuecong XuTemporal Domain Adaptation Unsupervised Domain Adaptation
National University of Singapore · The University of Tokyo
CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation
Aug 10, 2026Felix Ott, Christopher MutschlerUnsupervised Domain Adaptation Domain Adaptation
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany · Machine Learning and Positioning Systems Department, University of Technology Nürnberg (UTN), 90461 Nürnberg
Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift
Jul 6, 2026Shuochen Wang, Nishant Yadav, Auroop R. GangulyDistribution Shift Robustness Domain Adaptation
Sustainability and Data Sciences Laboratory, Northeastern University · Microsoft · AI for Climate and Sustainability, The Institute for Experiential AI, Northeastern University
Temporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptation
Jun 22, 2026Shichang Meng, Linquan Wu, Xuan Ai +1Source-Free Domain Adaptation Domain Adaptation
City University of Hong Kong Hong Kong, China
Robust OT-Guided Generative Residual Domain Adaptation for Bike-Sharing Demand Prediction under Temporal Domain Shift
May 22, 2026Yiming MaDomain Adaptation Traffic Forecasting
Department of Statistics and Finance, School of Management, University of Science and Technology of China
Tokenizer Fertility and Zero-Shot Performance of Foundation Models on Ukrainian Legal Text: A Comparative Study
May 14, 2026Volodymyr OvcharovMultilingual Language Model Evaluation Legal NLP
LEX AI Platform, legal.org.ua Kyiv, Ukraine
Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation
Apr 23, 2026Weisi Liu, Guangzeng Han, Xiaolei HuangDomain Adaptation Retrieval-Augmented Classification
University of Memphis
Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time
Mar 20, 2026Sooyoung Jeon, Hongjie Tian, Lemeng Wang +7Temporal Domain Adaptation Wildlife Monitoring
The Ohio State University · Boston University
STAMP: Symmetric Temporal Averaging Message Passing for Domain Adaptation in Chronologically Split Graphs
Nov 17, 2024Joo Young Park, Sejun Park, Hyunwoo ParkTemporal Domain Adaptation Domain Adaptation
College of Medicine, Seoul National University, Gwanak-gu, Seoul 08826, Korea · Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul 08826, Korea · Graduate School of Data Science, Seoul National University, Gwanak-gu, Seoul 08826, Korea

Oct 5, 2026J/K move · Enter open · S save

Conditional Flow Matching for Transport Between Markov Processes

Syamantak Kumar, Dheeraj Nagaraj, Saptarshi Roy, Purnamrita Sarkar

The University of Texas at Austin, Department of Computer Science · Google Deepmind · Mohamed bin Zayed University of Artificial Intelligence, CMS Division+1

Conditional Flow Matching Domain Adaptation Markov Models Optimal Transport Temporal Domain Adaptation

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Motivated by sequence-to-sequence transport in the context time-series domain adaptation, we study the problem of transportation between trajectories of Markov processes. Given a limited number of trajectories from source distribution and the target distribution, we formulate a flow matching based algorithm which learns a transport map from the source to target trajectory distribution, while preserving the Markov structure. We show that this is consistent in the population limit and derive finite-sample error bounds under mixing time assumptions, following the analysis of classical statistical problems including regression (Nagaraj et al., 2020), principal component analysis (Kumar and Sarkar, 2023), and matrix concentration (Neeman et al., 2024) in the Markov setting. We complement that with a lower-bound construction showing that a mixing-time dependent sample complexity is unavoidable even with regular Gaussian conditional transitions. We evaluate on synthetic and real-world data. For image retrieval from electroencephalography (EEG) on THINGS-EEG2 (Gifford et al., 2022), the task is to identify the viewed image from EEG signals captured from human subjects, which suffers from high inter subject variability. We augment the ENIGMA decoder (Kneeland et al., 2026) with a conditional flow before its subject-specific temporal map. This improves mean top-5 retrieval accuracy from 43.87% to 49.05%, an 11.82% relative improvement.