Christina Chen

TL;DR: This work shows the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain, and shows that this problem appears in a wide variety of practical ML pipelines.

TL;DR: In this paper , the authors explore the settings in which recently proposed mitigation strategies are applicable by referring to a causal framing, and show that real-world applications are complex and often invalidate the assumptions of such methods.

TL;DR: In this article , the authors adopt a causal framing to motivate conditional independence tests as a key tool for characterizing distribution shifts, which can help diagnose failures of fairness transfer, including cases where real-world shifts are more complex than is often assumed in the literature.

TL;DR: This work used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predict multiple sclerosis disease progression, and results are the first to show that it is possible to predict disease progression.

TL;DR: It is shown how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions, and a joint model of intervention policy and adverse event risk is proposed.