Prompted by these insights, we reviewed over 1.5 million scientific papers to understand the origin of this invalid

However, the imagined transitions may be inaccurate, thus downgrading the performance of the underlying offline RL method.

We consider two scalings of the first step learning rate η . For small η, we establish a Gaussian equivalence property for the trained feature map, and prove that the learned kernel improves upon the initial random feature model, but cannot defeat the best linear model on the input.

There are mainly two notable types of SGMs which are inherent for different learning problems.

In this way, even with limited meta samples, MTG-Net holds the potential to produce reasonable gain estimations on arbitrary task combinations.

Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks.

Consider a bandit algorithm that recommends actions to self-interested users in a recommendation system. The users are free to choose other actions and need to be incentivized to follow the algorithm's recommendations.