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Found 11 papers, 10 papers with code
Detecting critical treatment effect bias in small subgroups
Randomized trials are considered the gold standard for making informed decisions in medicine, yet they often lack generalizability to the patient populations in clinical practice.
Privacy-preserving data release leveraging optimal transport and particle gradient descent
We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government.
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
Further, we show how our lower bound can correctly identify the absence and presence of unobserved confounding in a real-world setting.
Strong inductive biases provably prevent harmless interpolation
Classical wisdom suggests that estimators should avoid fitting noise to achieve good generalization.
Tight bounds for maximum $\ell_1$-margin classifiers
In particular, for the noiseless setting, we prove tight upper and lower bounds for the prediction error that match existing rates of order $\frac{\|w^*\|_1^{2/3}}{n^{1/3}}$ for general ground truths.
Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias
Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator.
Tight bounds for minimum l1-norm interpolation of noisy data
We provide matching upper and lower bounds of order $\sigma^2/\log(d/n)$ for the prediction error of the minimum $\ell_1$-norm interpolator, a. k. a.
Interpolation can hurt robust generalization even when there is no noise
Numerous recent works show that overparameterization implicitly reduces variance for min-norm interpolators and max-margin classifiers.
Maximizing the robust margin provably overfits on noiseless data
Numerous recent works show that overparameterization implicitly reduces variance, suggesting vanishing benefits for explicit regularization in high dimensions.
How rotational invariance of common kernels prevents generalization in high dimensions
Kernel ridge regression is well-known to achieve minimax optimal rates in low-dimensional settings.
Efficient Smoothing of Dilated Convolutions for Image Segmentation
Dilated Convolutions have been shown to be highly useful for the task of image segmentation.
