34 citations · 45 across the 7 of their papers we have counts for
Showing 2022Show all
2 papers · 1 filter
cs.LG2022★ 3 cited
Gradient Boosting Performs Gaussian Process Inference
Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Rid…
cs.LG2022★ 1 cited
Which Tricks Are Important for Learning to Rank?
Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin +1
Nowadays, state-of-the-art learning-to-rank methods are based on gradient-boosted decision trees (GBDT). The most well-known algorithm is LambdaMART which was proposed more than a…