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Found 4 papers, 3 papers with code
A Fast Algorithm for Adaptive Private Mean Estimation
We design an $(\varepsilon, \delta)$-differentially private algorithm to estimate the mean of a $d$-variate distribution, with unknown covariance $\Sigma$, that is adaptive to $\Sigma$.
Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification
While a broad range of techniques have been proposed to tackle distribution shift, the simple baseline of training on an $\textit{undersampled}$ balanced dataset often achieves close to state-of-the-art-accuracy across several popular benchmarks.
Is Importance Weighting Incompatible with Interpolating Classifiers?
As a remedy, we show that polynomially-tailed losses restore the effects of importance reweighting in correcting distribution shift in overparameterized models.
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Our BCOP parameterization allows us to train large convolutional networks with provable Lipschitz bounds.
