TabPFN is a Prior-Data Fitted Network that approximates Bayesian inference for small tabular classification by training a Transformer once on synthetic data drawn from a causal prior, then solves new tasks in a single forward pass without further updates.
Rothenhäusler, N
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
CONDITIONAL 2representative citing papers
Increased regularization is required for group DRO to achieve good worst-group generalization in overparameterized neural networks.
citing papers explorer
-
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
TabPFN is a Prior-Data Fitted Network that approximates Bayesian inference for small tabular classification by training a Transformer once on synthetic data drawn from a causal prior, then solves new tasks in a single forward pass without further updates.
-
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Increased regularization is required for group DRO to achieve good worst-group generalization in overparameterized neural networks.