28 citations · 40 across the 4 of their papers we have counts for
6 papers
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…
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…
Predicting on the Edge: Identifying Where a Larger Model Does Better
Taman Narayan, Heinrich Jiang, Sen Zhao +1
Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity…
Regularization Strategies for Quantile Regression
Taman Narayan, Serena Wang, Kevin Canini +1
We investigate different methods for regularizing quantile regression when predicting either a subset of quantiles or the full inverse CDF. We show that minimizing an expected pinb…
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter, Heinrich Jiang, Serena Wang +4
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study th…
Interpretable Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang +4
We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice net…