66 citations · 104 across the 11 of their papers we have counts for
11 papers
DeepROCK: Error-controlled interaction detection in deep neural networks
Winston Chen, William Stafford Noble, Yang Young Lu
The complexity of deep neural networks (DNNs) makes them powerful but also makes them challenging to interpret, hindering their applicability in error-intolerant domains. Existing…
Bounding the FDP in competition-based control of the FDR
Arya Ebadi, Dong Luo, Jack Freestone +2
Competition-based approach to controlling the false discovery rate (FDR) recently rose to prominence when, generalizing it to sequential hypothesis testing, Barber and Candès used…
Competition-based control of the false discovery proportion
Dong Luo, Arya Ebadi, Yilun He +3
Recently, Barber and Candès laid the theoretical foundation for a general framework for false discovery rate (FDR) control based on the notion of "knockoffs." A closely related FDR…
DANCE: Enhancing saliency maps using decoys
Yang Lu, Wenbo Guo, Xinyu Xing +1
Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most stron…
Multiple competition-based FDR control for peptide detection
Kristen Emery, Syamand Hasam, William Stafford Noble +1
Competition-based FDR control has been commonly used for over a decade in the computational mass spectrometry community (Elias and Gygi, 2007). Recently, the approach has gained si…
apricot: Submodular selection for data summarization in Python
Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble
We present apricot, an open source Python package for selecting representative subsets from large data sets using submodular optimization. The package implements an efficient greed…