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Implicit Regularization Paths of Weighted Neural Representations
Subsampling involves creating smaller datasets by randomly selecting a subset of the original data points.
ProbabilisticMarginsforInstanceReweighting inAdversarialTraining
In this case, one can hardly distinguish non-robust data with LPS being 10 and safe data that are insensitive to be attacked.
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
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.
Learning Human-like Representations to Enable Learning Human Values Andrea H. Wynn
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.