134 citations · 330 across the 35 of their papers we have counts for
9 papers · 1 filter
Hedging on the Frontier: Learning New Tasks with Few Samples
Tobias Wegel, Federico Di Gennaro, Geelon So +1
When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model evaluations on related tasks i…
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser +1
In the era of fast-paced precision medicine, observational studies play a major role in properly evaluating new treatments in clinical practice. Yet, unobserved confounding can sig…
Strong inductive biases provably prevent harmless interpolation
Michael Aerni, Marco Milanta, Konstantin Donhauser +1
Classical wisdom suggests that estimators should avoid fitting noise to achieve good generalization. In contrast, modern overparameterized models can yield small test error despite…
Tight bounds for maximum -margin classifiers
Stefan Stojanovic, Konstantin Donhauser, Fanny Yang
Popular iterative algorithms such as boosting methods and coordinate descent on linear models converge to the maximum -margin classifier, a.k.a. sparse hard-margin SVM, in…
Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias
Konstantin Donhauser, Nicolo Ruggeri, Stefan Stojanovic +1
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. Even…
Interpolation can hurt robust generalization even when there is no noise
Konstantin Donhauser, Alexandru Ţifrea, Michael Aerni +2
Numerous recent works show that overparameterization implicitly reduces variance for min-norm interpolators and max-margin classifiers. These findings suggest that ridge regulariza…