134 citations · 330 across the 35 of their papers we have counts for
7 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…
Can semi-supervised learning use all the data effectively? A lower bound perspective
Alexandru Ţifrea, Gizem Yüce, Amartya Sanyal +1
Prior works have shown that semi-supervised learning algorithms can leverage unlabeled data to improve over the labeled sample complexity of supervised learning (SL) algorithms. Ho…
How robust accuracy suffers from certified training with convex relaxations
Piersilvio De Bartolomeis, Jacob Clarysse, Amartya Sanyal +1
Adversarial attacks pose significant threats to deploying state-of-the-art classifiers in safety-critical applications. Two classes of methods have emerged to address this issue: e…
PILLAR: How to make semi-private learning more effective
Francesco Pinto, Yaxi Hu, Fanny Yang +1
In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public unlabelled and private labelled data. We propose a computationally efficient algorithm that, un…
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…