2 citations · 2 across the 1 of their papers we have counts for
2 papers
stat.ML2022★ 2 cited
Q-learning with online random forests
Joosung Min, Lloyd T. Elliott
-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of -learning requires approximation of the state-action value function (also known a…
stat.ML2019
Random Tessellation Forests
Shufei Ge, Shijia Wang, Yee Whye Teh +2
Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursi…