A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A ".

Deep neural networks are the state-of-the-art for various applications. However,one of the biggest challenges facing them is the lack of labeled data to train these complex networks.

Suppose l is -lipschitzand l-smoothforthefirstargument (i.e.

In this work, we extend the above direction to a combinatorial semi-bandit setting and study avariant of stochastic MAB, where arms are subject to matroid constraints and each arm becomes unavailable (blocked) for afixed number of rounds after each play. A natural common generalization of the state-of-the-art for blocking bandits, and that for matroid bandits, only guarantees a1/2-approximation for general matroids.