arXiv:2501.18581v5 Announce Type: replace
Abstract: Bias-variance decompositions are widely used to understand the generalization performance of machine learning models. While the squared error loss...
By Tom Heskes
arXiv:2608. 10470v1 Announce Type: new Abstract: Fair representation learning with a continuous sensitive attribute $S$ requires a representation $Z$ that is statistically independent of $S$.
By Yijin Ni, Xiaoming Huo
arXiv:2104. 11547v5 Announce Type: replace-cross Abstract: Statistical models contain variables that are not random: parameters, treatments, environments, design points.
By Patrick Forr\'e
arXiv:2606. 29693v1 Announce Type: new Abstract: We ask a simple question about decoder-only transformers: \emph{between which two layers is the probability of a predicted token actually produced?
By Duc Anh Nguyen
arXiv:2608. 25138v1 Announce Type: new Abstract: Stochastic masking, cropping, or modality removal makes deterministic reconstruction an incomplete target: one observation can admit many clean completions.
By Jiarui Cao
arXiv:2608. 15215v1 Announce Type: cross Abstract: Token-level knowledge distillation (KD) matches two conditional distributions per position, yet the standard objectives compare them pointwise: a Kullback-Leibler gradient is blind to which wrong token receives probability mass.
By Gordei Verbii, Juho Lee