Search Results for author:
Found 5 papers, 2 papers with code
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed.
An Adversarial Robustness Perspective on the Topology of Neural Networks
In this paper, we investigate the impact of neural networks (NNs) topology on adversarial robustness.
Origins of Low-dimensional Adversarial Perturbations
Finally, we show that if a decision-region is compact, then it admits a universal adversarial perturbation with $L_2$ norm which is $\sqrt{d}$ times smaller than the typical $L_2$ norm of a data point.
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i. e. realizations of a random permutation $\Sigma$ of a finite set, $\{1,\; \ldots,\; n\}$ with $n\geq 1$ say.
Adversarial Robustness via Label-Smoothing
We study Label-Smoothing as a means for improving adversarial robustness of supervised deep-learning models.
