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20182026
most citedOptimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

28 citations · 40 across the 4 of their papers we have counts for

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2 papers · 1 filter

cs.LG2026

TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models

Deqing Fu, Huangyuan Su, Rajat Sen +4

Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliar…

cs.LG2026

TabFM: A Zero-Shot Foundation Model for Tabular Data

Weihao Kong, Erez Louidor Ilan, Shuxin Nie +6

Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We present TabFM, a 400M-paramete…