24 citations · 31 across the 5 of their papers we have counts for
5 papers
Partial Identifiability in Discrete Data With Measurement Error
Noam Finkelstein, Roy Adams, Suchi Saria +1
When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest. These assumptions sho…
The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods
Roy Adams, Suchi Saria, Michael Rosenblum
The use of observational time series data to assess the impact of multi-time point interventions is becoming increasingly common as more health and activity data are collected and…
Evaluating Model Robustness and Stability to Dataset Shift
Adarsh Subbaswamy, Roy Adams, Suchi Saria
As the use of machine learning in high impact domains becomes widespread, the importance of evaluating safety has increased. An important aspect of this is evaluating how robust a…
Learning Models from Data with Measurement Error: Tackling Underreporting
Roy Adams, Yuelong Ji, Xiaobin Wang +1
Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to infor…
Learning Time Series Detection Models from Temporally Imprecise Labels
Roy J. Adams, Benjamin M. Marlin
In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a…