3 accepted papers
Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context size. Deep neural networks remain a highly competitive and more efficient modeling paradigm when equipped with well-desi…
Electronic health records (EHR) aggregate extensive data critical for advancing patient care and refining intervention strategies. EHR data is essential for epidemiological study, more commonly referred to as cohort study, where patients with shared characteristics or similar diseases are analyzed o…
Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed. Unfortunately, this assumption is unrealistic in large-scale MLC data that has long-tailed (LT) distribution and partial labels (PL). To address the problem, we introduce a…