7 citations · 13 across the 3 of their papers we have counts for
3 papers · 1 filter
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data
Oscar Giles, Kasra Hosseini, Grigorios Mingas +13
Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been sho…
Kernel Stein Tests for Multiple Model Comparison
Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf +1
We address the problem of non-parametric multiple model comparison: given candidate models, decide whether each candidate is as good as the best one(s) or worse than it. We pro…
More Powerful Selective Kernel Tests for Feature Selection
Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum +3
Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical a…