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cs.LG2026
Thinking in Depth: Retrospective Inference for Tabular Foundation Models
Hao-Run Cai, Si-Yang Liu, Zi-Jian Cheng +8
Tabular foundation models (TFMs) are pretrained across diverse tabular tasks and make predictions on a new table at inference time using its labeled examples as context. Most recen…
cs.LG2026
Rethinking LLM Ensembling from the Perspective of Mixture Models
Jiale Fu, Yuchu Jiang, Peijun Wu +3
Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multi…