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Found 4 papers, 1 papers with code
U-Statistics for Importance-Weighted Variational Inference
We propose the use of U-statistics to reduce variance for gradient estimation in importance-weighted variational inference.
Causal Inference using Gaussian Processes with Structured Latent Confounders
Latent confounders---unobserved variables that influence both treatment and outcome---can bias estimates of causal effects.
Resilience of Supervised Learning Algorithms to Discriminatory Data Perturbations
In this study, we i) define and model discrimination as perturbations of a data-generating process and show how discrimination can be induced via attributes correlated with the protected attributes; ii) introduce a measure of resilience of a supervised learning algorithm to potentially discriminatory data perturbations, iii) propose a novel supervised learning algorithm that inhibits discrimination, and iv) show that it is more resilient to discriminatory perturbations in synthetic and real-world datasets than state-of-the-art learning algorithms.
A General Framework for Counterfactual Learning-to-Rank
Specifically, we derive a relaxation for propensity-weighted rank-based metrics which is subdifferentiable and thus suitable for gradient-based optimization.
