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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.
Learning from Discriminatory Training Data
Supervised learning systems are trained using historical data and, if the data was tainted by discrimination, they may unintentionally learn to discriminate against protected groups.
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.
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