4 papers
Reconciling Universal and Uniform Learning with -Aggregation
Mikael Møller Høgsgaard, Patrick Rebeschini, Tobias Wegel
We study regression under bounded responses in terms of excess mean squared error. When the comparator class is finite, this setting is known as model selection aggregation, and ac…
Aggregation with Exponential Weights is Optimal in Expectation
Mikael Møller Høgsgaard, Patrick Rebeschini, Tobias Wegel
The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss. In particular, whether it is…
Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression
Tobias Wegel, Gil Kur, Patrick Rebeschini
We study early-stopped mirror descent (ESMD) for high-dimensional Gaussian linear regression over arbitrary convex bodies and design matrices, where the task is to minimize the in-…
A Framework for Verification of Wasserstein Adversarial Robustness
Tobias Wegel, Felix Assion, David Mickisch +1
Machine learning image classifiers are susceptible to adversarial and corruption perturbations. Adding imperceptible noise to images can lead to severe misclassifications of the ma…