As efficient exploration in BAMDPs hinges upon the judicious acquisition of information, our complexity measure highlights the worst-case difficulty of gathering information and exhausting epistemic uncertainty.

Meta-learning is a promising strategy for learning to efficiently learn using data gathered from a distribution of tasks.

For many applications, variables in the data can be gathered into semantically meaningful groups, where useful insights are at group level. For example, in finance, one may be interested in how a financial situation influences different industries (i.e.

Towards bridging this gap, we present a unifying perspective where several such approaches can be viewed as imposing a regularization on the representation via a learnable function using unlabeled data.

Specifically, we detail how baseline models are tuned in link prediction tasks as follows.

However, XLNet does not leverage the full position information of a sentence and thus suffers from position discrepancy between pre-training and fine-tuning.

Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents.