Subsampling involves creating smaller datasets by randomly selecting a subset of the original data points.

Our PMs are continuous and path-independent, overcoming the deficiencyofpreviousworks[47]. Moreover, there is still room for improvement in our approach and related works. This paper mainly focuses on adversarial robustness regarding white-box attacks generated by the first-order gradient-based methods. When employing our MAIL in real-world applications, it may lead to over-confidence regarding many other attacks, e.g., provable attacks [5], black-box attacks [6], and physical attacks [25]. For data assigned with larger weights, the resulting model would be more robust when encounters similar dataduring thetest. This unfairness problem seems inevitable forareweighted learning framework, which will interest our further study.

Modern large scale datasets are often plagued with missing entries. For tabular data with missing values, a flurry of imputation algorithms solve for a complete matrix which minimizes some penalized reconstruction error. However, almost none of them can estimate the uncertainty of its imputations. This paper proposes a probabilistic and scalable framework for missing value imputation with quantified uncertainty.

How can we build AI systems that can learn any set of individual human values both quickly and safely, avoiding causing harm or violating societal standards for acceptable behavior during the learning process? We explore the effects of representational alignment between humans and AI agents on learning human values.