We explore the minimax optimal error associated with a demographic parity-constrained regression problem within the context of a linear model.

Dataset distillation can be formulated as a bi-level meta-learning problem where the outer loop optimizes the metadataset and the inner loop trains a model on the distilled data.

Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents.

Foundation models [6] such as CLIP [47] have been extensively utilized on diverse domains via pretrain-finetune approaches.