Here, we show that marginal variance tends to increase along the causal order for generically sampled additivenoise models.

Verification of neural networks enables us to gauge their robustness against adversarial attacks. Verification algorithms fall into two categories:exact verifiers that run in exponential time andrelaxed verifiers that are efficient but incomplete. In this paper, we unify all existing LP-relaxed verifiers, to the best of our knowledge, under a general convex relaxation framework.

Moreover, they are primarily collected from limited laboratory scenes with textual descriptions manually labeled, which greatly limits their scalability.

Neural Information Processing Systems http://nips.cc/

In almost all dueling bandit applications, the decision space often changes over time; eg, retail store management, onlineshopping,restaurantrecommendation,searchengineoptimization,etc.