Firstly, these methods implicitly assume that all group combinations are represented during training. To illustrate this, we introduce a systematic generalization task on the MPI3D dataset and discover that current algorithms fail to improve the ERM baseline when combinations of observed attribute values are missing.

In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g.,

Note that this is defined up to an absolute phase, i.e. Learning models In this section we first describe the learning models we will be concerned with in this paper. Such quantum examples have been investigated in prior works [6, 8, 9]. A natural way to extend the learning model is to allow the algorithm quantum statistical queries . QSQ model allows a quantum advantage in learning in this framework.

Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data.

M1: Chunking long documents into max-context-length chunks, and averaging all-mini-LM-v6 embeddings across chunks to produce a final document embedding.

A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective In this appendix, we will derive Eq. 4. Analogously to Eq. 3, we optimize the following objective: max