Statistical Bias in Dataset Replication

ICML 2020 Logan EngstromAndrew IlyasShibani SanturkarDimitris TsiprasJacob SteinhardtAleksander Madry

Dataset replication is a useful tool for assessing whether models have overfit to a specific validation set or the exact circumstances under which it was generated. In this paper, we highlight the importance of statistical modeling in dataset replication: we present unintuitive yet pervasive ways in which statistical bias, when left unmitigated, can skew results... (read more)

PDF ICML 2020 PDF

Code


No code implementations yet. Submit your code now

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet