The current methods for estimating the nadir objective vector perform effectivelyonly onspecific MOPs.

Thisworkdiscusses howtoachieveworst-case Out-Of-Distribution(OOD) generalization for avariety of distributions based on arelatively small labeling cost.

Thebrain prepares forlearning evenbefore interacting withtheenvironment, by refining and optimizing its structures through spontaneous neural activity that resembles random noise. However,the mechanism of such aprocess has yet to be understood, and it is unclear whether this process can benefit the algorithm of machine learning.

TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout.

Federated Learning (FL) [1]provides acommunication-efficient andprivacypreserving means to learn from data distributed across clients such as cell phones, autonomous vehicles, and hospitals. FL aims for each client to benefit from collaborating in the learning process without sacrificing data privacy or paying a substantial communication cost. Federated Averaging (FedAvg) [1] is the predominant FL algorithm.