4 papers
Negatives-Dominant Contrastive Learning for Generalization in Imbalanced Domains
Meng Cao, Jiexi Liu, Songcan Chen
Imbalanced Domain Generalization (IDG) focuses on mitigating both domain and label shifts, both of which fundamentally shape the model's decision boundaries, particularly under het…
Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled time series (ISTS), characterized by non-uniform time intervals with natural missingness, are prevalent in real-world applications. Existing approaches for ISTS…
TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among s…
Guidance Not Obstruction: A Conjugate Consistent Enhanced Strategy for Domain Generalization
Meng Cao, Songcan Chen
Domain generalization addresses domain shift in real-world applications. Most approaches adopt a domain angle, seeking invariant representation across domains by aligning their mar…