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The part I found tricky in supporting quantiles is that as the loss isn't differentiable per se, I ended up tracking full vector in observations/targets in order to compute the leaf prediction values. Having a reliable gradient based proxy would be preferable. Notably for maintenance as I for now don't have much use case with such loss objectives.
If you can point to a gradient based approach for estimating the loss/gain and associated leaf predictions, it should be fairly strightforward to add support for this loss.
It would be nice to have multi-quantile regression for approximating histogram in one go.
similar to
https://catboost.ai/en/docs/concepts/loss-functions-regression#MultiQuantile
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