Accuracy
On Making Stochastic Classifiers Deterministic
Andrew Cotter, Maya Gupta, Harikrishna Narasimhan
Neural Information Processing Systems http://nips.cc/
Supplementary material - ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods
We used the sex and the education of the student's parents as the sensitive attributes for this dataset. We removed all features that are other expressions of the labels (i.e. Note that this is the only folktables dataset on which we report results in the main paper. Sex, age, and rage are used as sensitive features for this datasets. We deem these features as not relevant for this use case.
Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data
The assumption that data are independently and identically distributed (IID) is staple in statistical machine learning. It suggests that a hypothesis selected by an algorithm, after observing several training samples, should perform effectively on test samples from the same unknown distribution.
Random Feature Stein Discrepancies
Jonathan Huggins, Lester Mackey
Neural Information Processing Systems http://nips.cc/
DistributedInverseConstrainedReinforcement LearningforMulti-agentSystems
We formally guarantee that the distributed learners asymptotically achieve consensus which belongs to the set of stationary points of the bi-level optimization problem.
Distributed Weight Consolidation: A Brain Segmentation Case Study
Patrick McClure, Charles Y. Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satra Ghosh, Dylan Nielson, Peter A. Bandettini, Francisco Pereira
Neural Information Processing Systems http://nips.cc/