Users can include new explanation methods into OpenXAI's list of

We find that this is often insufficient to capture the distribution of the original data, especially around the decision boundaries.

In particular, we show that fairwashed explanation models can generalize beyond the suing group ( i.e., data points that are being explained), meaning that

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.

We obtain low regret and efficient algorithms in the online setting, and generalization guarantees in the distributional setting.

What is the information leakage of an iterative randomized learning algorithm about its training data, when the internal state of the algorithm is private?

We now consider the gradient of the log-variance loss. Using the definition from Eq. 5, we see that From [Reiss, 2012, Lemma A.3.5] we have the bound Combining these estimates we arrive at the claimed result. The claim follows by direct calculation. In fact, it is possible to take K = 15 . Lemma 3 shows that the kurtosis term in our bound Eq. 16 can be bounded for Gaussian families.