Although significant efforts have been made into tackling statistical data heterogeneity,the diversity in the processing capabilities andnetworkbandwidth ofclients,termedassystemheterogeneity,hasremained largelyunexplored.

We here argue that popular benchmarks to measure model robustness againstcommon corruptions (likeImageNet-C) underestimate model robustness in many (but not all) application scenarios. The key insight is that inmanyscenarios, multiple unlabeled examples ofthe corruptions are available and can be used for unsupervised online adaptation.

Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-levelperceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes.

Existing approaches based on off-policyevaluation (OPE) oftenrequireadditional function approximation and hence hyperparameters, creating a chicken-and-egg situation.

The recent large-scale text-to-image generative models have attained unprecedented performance, while people establishedadaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distributionconcepts. This failure is highly related to the low quality of training data.