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cs.LG2026
Interweaving Marginals into Multivariate Sample Paths: Training-Free Dependence Construction for Probabilistic Time Series Foundation Models
Jinmyeong Choi, Jinkwan Jang, Seul Lee +1
Probabilistic time series foundation models (TSFMs) provide coordinate-wise predictive distributions, but these marginals do not determine a joint distribution over multivariate fu…
cs.LG2025
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi +1
Real-world time series data are inherently multivariate, often exhibiting complex inter-channel dependencies. Each channel is typically sampled at its own period and is prone to mi…
cs.LG2025
Contrastive Residual Energy Test-time Adaptation
Yewon Han, Seoyun Yang, Taesup Kim
Test-time adaptation (TTA) enhances model robustness by enabling adaptation to target distributions that differ from training distributions, improving real-world generalizability.…