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da8f9fc2b555d122369f36a9684415c1-Paper-Conference.pdf
Is the whole more than the sum of its parts?
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.
Bayesian Structure Learning by Recursive Bootstrap
Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik
Neural Information Processing Systems http://nips.cc/
VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
Near-OptimalCorrelationClusteringwithPrivacy
Then the correlation clustering objective asks to minimize the sum of (the weights of) positive edges across clusters plus the sum of (the weights) of negative edges within clusters.
Multi-Class Learning: From Theory to Algorithm
Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang
Moreover,the proposed multi-class kernel learning algorithms have statistical guarantees and fast convergence rates. Experimental results on lots of benchmark datasets show that our proposed methods can significantly outperform the existing multi-class classification methods. The major contributions ofthispaper include: 1)Anewlocal Rademacher complexitybased bound withfastconvergence rate for multi-class classification is established. Existing works [16,27] for multi-class classifiers with Rademacher complexity does not take into account couplings among different classes.