Statistical Learning
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training Y anlai Y ang 1, Matt Jones
The behavior emerges and becomes more robust as the architecture scales up its number of parameters.
Adapting Fairness Interventions to Missing Values
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.
Bayes-optimal learning of an extensive-width neural network from quadratically many samples
Technically, our result is enabled by establishing a link with recent works on optimal denoising of extensive-rank matrices and on the ellipsoid fitting problem.
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
This illustrates the dependence of the classifier on the representation's robustness, i.e., the predictor can only be robust if
effres_project (17).pdf
Effective resistances have a broad range of algorithmic implications.
