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cs.LG2020★ 70 cited
Learning explanations that are hard to vary
Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto +2
In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning. We show that averaging gradients across examples -- akin to a…
cs.LG2019★ 1 cited
Privacy-Preserving Causal Inference via Inverse Probability Weighting
Si Kai Lee, Luigi Gresele, Mijung Park +1
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sci…