The authors recently gave an $n^{O(\log\log n)}$ time membership query algorithm for properly learning decision trees under the uniform distribution (Blanc et al., 2021). The previous fastest algorithm for this problem ran in $n^{O(\log n)}$ time, a consequence of Ehrenfeucht and Haussler (1989)'s classic algorithm for the distribution-free setting. In this article we highlight the natural open problem of obtaining a polynomial-time algorithm, discuss possible avenues towards obtaining it, and state intermediate milestones that we believe are of independent interest.
翻译:作者最近给出了美元( log\ log n) 时间成员查询算法, 用于在统一分布下正确学习决策树( Blanc et al., 2021) 。 这个问题的上一个最快算法以美元( log n) 美元计时, 这是Ehrenfeucht 和 Haussler (1989) 用于无分配环境的经典算法的结果。 在本篇文章中,我们强调了获得多纪念时间算法的自然开放问题, 讨论了获得这种算法的可能途径, 并说明了我们认为具有独立利益的中间里程碑 。