3 accepted papers
We present a method for identifying groups of test examples---slices---on which a model under-performs, a task now known as slice discovery. We formalize coherence---a requirement that erroneous predictions, within a slice, should be wrong for the same reason---as a key property that any slice disco…
In the Multiple Instance Learning (MIL) scenario, the training data consists of instances grouped into bags. Bag labels indicate whether each bag contains at least one positive instance, but instance labels are not observed. Recently, Haussmann et al (CVPR 2017) tackled the MIL instance label predic…
Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are ins…