Quantile-based distributional RL agents are usually improved by using alternate L2-based losses, at the cost of theoretical validity and distributional collapse. We propose a way to leverage effective L2 losses while maintaing an estimation of the full distribution of returns.
We tried to reproduce famous unbiased learning-to-rank results on the large Baidu-ULTR dataset. It turned out that it does not work as intended ... or at least that success depends on how your measure success.
Click models aim to identify and extract biases in search data, but we do not evaluate whether they actually do so. We propose a metric based on relevance annotations that measures the debiasedness of click models, and consequently their usefulness in learning-to-rank from implicit feedback.