6 citations · 17 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
Certified private data release for sparse Lipschitz functions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal +3
As machine learning has become more relevant for everyday applications, a natural requirement is the protection of the privacy of the training data. When the relevant learning ques…
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…