5 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
Bayes Complexity of Learners vs Overfitting
Grzegorz Głuch, Rudiger Urbanke
We introduce a new notion of complexity of functions and we show that it has the following properties: (i) it governs a PAC Bayes-like generalization bound, (ii) for neural network…
Noether: The More Things Change, the More Stay the Same
Grzegorz Głuch, Rüdiger Urbanke
Symmetries have proven to be important ingredients in the analysis of neural networks. So far their use has mostly been implicit or seemingly coincidental. We undertake a systemati…
Adversarial Robustness: What fools you makes you stronger
Grzegorz Głuch, Rüdiger Urbanke
We prove an exponential separation for the sample complexity between the standard PAC-learning model and a version of the Equivalence-Query-learning model. We then show that this s…
Query complexity of adversarial attacks
Grzegorz Głuch, Rüdiger Urbanke
There are two main attack models considered in the adversarial robustness literature: black-box and white-box. We consider these threat models as two ends of a fine-grained spectru…
Constructing a provably adversarially-robust classifier from a high accuracy one
Grzegorz Głuch, Rüdiger Urbanke
Modern machine learning models with very high accuracy have been shown to be vulnerable to small, adversarially chosen perturbations of the input. Given black-box access to a high-…