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Decision trees provide a rich family of highly non-linear but efficientmodels, due to which they continue to be the go-to family of predictive modelsby practitioners across domains. But learning trees is challenging due to theirdiscrete decision boundaries. The state-of-the-art (SOTA) techniques resort to(a) learning \textit{soft} trees thereby losing logarithmic inference time; or(b) using methods tailored to specific supervised learning settings, requiringaccess to labeled examples and loss function. In this work, by leveragingtechniques like overparameterization and straight-through estimators, wepropose a unified method that enables accurate end-to-end gradient based treetraining and can be deployed in a variety of settings like offline supervisedlearning and online learning with bandit feedback. Using extensive validationon standard benchmarks, we demonstrate that our method provides best of bothworlds, i.e., it is competitive to, and in some cases more accurate thanmethods designed \textit{specifically} for the supervised settings; and inbandit settings, where most existing tree learning techniques are notapplicable, our models are still accurate and significantly outperform theapplicable SOTA methods.

Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent