Deep neural networks are known to be vulnerable to adversarial attacks: ...
In this paper, we study the problem of consistency in the context of
adv...
We propose a new computationally efficient test for conditional independ...
The Lipschitz constant of neural networks has been established as a key
...
Parallel black box optimization consists in estimating the optimum of a
...
This paper investigates the theory of robustness against adversarial att...
This paper tackles the problem of adversarial examples from a game theor...
We propose a new defense mechanism against adversarial attacks inspired ...
It has been empirically observed that defense mechanisms designed to pro...
Existing studies in black-box optimization suffer from low generalizabil...
We introduce an extension of the optimal transportation (OT) problem whe...
Design of experiments, random search, initialization of population-based...
Choosing the right selection rate is a long standing issue in evolutiona...
Contextual bandit algorithms are applied in a wide range of domains, fro...
We introduce a new black-box attack achieving state of the art performan...
Since the discovery of adversarial examples in machine learning, researc...
This paper investigates the theory of robustness against adversarial att...