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Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities

We cannot guarantee that training datasets are representative of the distribution of inputs that will be encountered during deployment. So we must have confidence that our models do not over-rely on this assumption. To this end, we introduce a new …

Ranking Policy Decisions

Policies trained via Reinforcement Learning (RL) are often needlessly complex, making them more difficult to analyse and interpret. In a run with _n_ time steps, a policy will decide _n_ times on an action to take, even when only a tiny subset of …

Adaptive Generation of Unrestricted Adversarial Inputs

Neural networks are vulnerable to adversarially-constructed perturbations of their inputs. Most research so far has considered perturbations of a fixed magnitude under some $l\_p$ norm. Although studying these attacks is valuable, there has been …