However, there are still settings where their zero-shot performance is far from optimal.

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.

We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner's objective is not convex-concave (and so the minimax theorem does not apply), we give a simple algorithm that can compete with the setting in which the adversary must announce their action first, with optimally diminishing regret.

(since we pick the log base to be 2). We now prove the converse statements. Individual fairness aims to ensure that "similar individuals are treated similarly." Predictive multiplicity allows different predictions from competing classifiers for the samples. Notably, neural networks with very narrows or wide layers have better reproducibility in their decision regions. The fact that multiple classifiers may yield distinct predictions to a target a sample while having statistically identical average loss performance can also cause security issues in machine learning.

Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data.