Machine comprehension (MC) has gained significant popularity over the past few years and it is a coveted goal in the field of natural language understanding. Its task is to teach the machine to understand thecontent ofagivenpassage andthenanswer arelated question, which requires deep comprehension and accurate information extraction towards the text.

Ninedifferentdebiasing algorithms (and a baseline) have been evaluated with this dataset using the popular ResNet-18 network[36]. CelebA contains faces of celebrities with several binary task labelsandtwoprotected labels(genderandyouth). Table 3showsthe prediction results from a biased binary classifier and its bias values using the seven metrics. Without losing generality, we consider "Sport" the positive class in the binary classifier. Following the DP formula in Appendix A.2, for the "Sport" class, thePPRfemale is 45.0% (90 /200), andPPRmale is65.0%

Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations.

InProceedingsofthe Networkand Distributed System Security Symposium, volume 14, pages 23-26, 2014.

Ensemble methodsand fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability.

The objective function is the composition of two expectations of stochastic functions, and ismore challenging tooptimize than vanilla stochastic optimization problems.

In the specific case of MuZero Reanalyse it also performs a tree search rather than a single step lookaheadusedinDyna.

Insuch applications, a team of agents aim to maximize a joint expected utility through a single global reward.

Our algorithm is based on a decomposition of the Barker acceptance test that allows evaluating the Rényi DP privacy cost of the acceptreject choice.

Machine learning aims to generalize models trained on given datasets to make accurate predictions or decisions on new, unseen data (El Naqa and Murphy, 2015). The effectiveness of those models depends on the alignment between the training datasets and deployment environments (Quinonero-Candela et al.,2008).