Neural Information Processing Systems http://nips.cc/

We give the first polynomial-time algorithm for robust regression in the listdecodable setting where an adversary can corrupt a greater than1/2 fraction ofexamples.

Neural Information Processing Systems http://nips.cc/

Alldatausedispublic.] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they werechosen)? A.1 TrainingDetails In our experiments, the classifierfθ is a 8-layer MLP with 128 hidden dimensions per layer.

Moreover, our robust coreset can be efficiently maintained in fullydynamic environment. To the best of our knowledge, this is the first robust and fully-dynamic coreset construction method for these optimization problems.

In this section we provide visualizations to better understand the statistical power of our test, and to verify the claims in Section 2.3. We can see that R constructed from outlier images generally include a higher proportion of unexplained semantic information: comparing the CelebA residual in Fig.3(a) (second column) where the model is trained on CIFAR-10, to Fig.3(b) (first column) where CelebA is inlier, we can see that the facial structure in CelebA residual is more evident when the model is trained on CIFAR-10. Similarly, comparing the CIFAR-10 residual from both models, we can see that the structure of the vehicle (e.g. As the residual sequences constructed from outliers tend to have more natural image-like structures, they will also have stronger spatial autocorrelations, compared with residuals from inlier samples that should in principle be white noise. Note that while the residual sequences constructed from inliers also contain unexplained semantic information, this is due to estimation error of the deep AR model, and should not happen should we have access to the ground truth model, as we have shown in Section 2.2.