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Impact of Fairness Regulations on Institutions' Policies and Population Qualifications
The proliferation of algorithmic systems has fueled discussions surrounding the regulation and control of their social impact.
On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms
Finally, we present several numerical results on the application of DP-based learning methods to standard centralized and distributed learning problems.
f-divergences and their applications in lossy compression and bounding generalization error
In this paper, we provide three applications for $f$-divergences: (i) we introduce Sanov's upper bound on the tail probability of the sum of independent random variables based on super-modular $f$-divergence and show that our generalized Sanov's bound strictly improves over ordinary one, (ii) we consider the lossy compression problem which studies the set of achievable rates for a given distortion and code length.
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms
Understanding generalization in modern machine learning settings has been one of the major challenges in statistical learning theory.
Learning under Distribution Mismatch and Model Misspecification
We study learning algorithms when there is a mismatch between the distributions of the training and test datasets of a learning algorithm.
Information Theory Information Theory
