S2MAM is a new semi-supervised model that uses bilevel optimization to automatically identify informative variables, update similarity matrices, and provide interpretable predictions with theoretical guarantees.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Representations learned by large AI models are converging toward a shared statistical model of reality.
citing papers explorer
-
S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
S2MAM is a new semi-supervised model that uses bilevel optimization to automatically identify informative variables, update similarity matrices, and provide interpretable predictions with theoretical guarantees.
-
The Platonic Representation Hypothesis
Representations learned by large AI models are converging toward a shared statistical model of reality.