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Title: Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Paper content:
Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech.
Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable.
In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems.
In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.
In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning.
With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks.
We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.
The recent rise of deep neural networks (DNN) resulted in a substantial breakthrough for a large number of machine learning tasks in computer vision, natural language processing, speech recognition, reinforcement learning (Goodfellow et al., 2016) .
Both gradient-based optimization via backpropagation (Rumelhart et al., 1985) and hierarchical representation learning appear to be crucial in increasing the performance of machine learning for these problems by a large margin.
While the superiority of deep architectures in these domains is undoubtful, machine learning for tabular data still did not fully benefit from the DNN power.
Namely, the state-of-the-art performance in problems with tabular heterogeneous data is often achieved by "shallow" models, such as gradient boosted decision trees (GBDT) (Friedman, 2001; Chen & Guestrin, 2016; Ke et al., 2017; Prokhorenkova et al., 2018) .
While the importance of deep learning on tabular data is recognized by the ML community, and many works address this problem (Zhou & Feng, 2017; Miller et al., 2017; Lay et al., 2018; Feng et al., 2018; Ke et al., 2018) , the proposed DNN approaches do not consistently outperform the state-of-the-art shallow models by a notable margin.
In particular, to the best of our knowledge, there is still no universal DNN approach that was shown to systematically outperform the leading GBDT packages (e.g., XGBoost (Chen & Guestrin, 2016) ).
As additional evidence, a large number of Kaggle ML competitions with tabular data are still won by the shallow GBDT methods (Harasymiv, 2015) .
Overall, at the moment, there is no dominant deep learning solution for tabular data problems, and we aim to reduce this gap by our paper.
We introduce Neural Oblivious Decision Ensembles (NODE), a new DNN architecture, designed to work with tabular problems.
The NODE architecture is partially inspired by the recent CatBoost package (Prokhorenkova et al., 2018) , which was shown to provide state-of-the-art performance on a large number of tabular datasets.
In a nutshell, CatBoost performs gradient boosting on oblivious decision trees (decision tables) (Kohavi, 1994; Lou & Obukhov, 2017) , which makes inference very efficient, and the method is quite resistant to overfitting.
In its essence, the proposed NODE architecture generalizes CatBoost, making the splitting feature choice and decision tree routing differentiable.
As a result, the NODE architecture is fully differentiable and could be incorporated in any computational graph of existing DL packages, such as TensorFlow or PyTorch.
Furthermore, NODE allows constructing multi-layer architectures, which resembles "deep" GBDT that is trained end-to-end, which was never proposed before.
Besides the usage of oblivious decision tables, another important design choice is the recent entmax transformation (Peters et al., 2019) , which effectively performs a "soft" splitting feature choice in decision trees inside the NODE architecture.
As discussed in the following sections, these design choices are critical to obtain state-of-the-art performance.
In a large number of experiments, we compare the proposed approach with the leading GBDT implementations with tuned hyperparameters and demonstrate that NODE outperforms competitors consistently on most of the datasets.
Overall, the main contributions of our paper can be summarized as follows:
1. We introduce a new DNN architecture for machine learning on tabular data.
To the best of our knowledge, our method is the first successful example of deep architectures that substantially outperforms leading GBDT packages on tabular data.
2. Via an extensive experimental evaluation on a large number of datasets, we show that the proposed NODE architecture outperforms existing GBDT implementations.
3. The PyTorch implementation of NODE is available online 1 .
The rest of the paper is organized as follows.
In Section 2 we review prior work relevant to our method.
The proposed Neural Oblivious Decision Ensembles architecture is described in Section 3 and experimentally evaluated in Section 4.
Section 5 concludes the paper.
In this paper, we introduce a new DNN architecture for deep learning on heterogeneous tabular data.
The architecture is differentiable deep GBDTs, trained end-to-end via backpropagation.
In extensive experiments, we demonstrate the advantages of our architecture over existing competitors with the default and tuned hyperparameters.
A promising research direction is incorporating the NODE layer into complex pipelines trained via back-propagation.
For instance, in multi-modal problems, the NODE layer could be employed as a way to incorporate the tabular data, as CNNs are currently used for images, or RNNs are used for sequences.
library to optimize Catboost, XGBoost, and FCNN hyperparameters.
For each method, we perform 50 steps of Tree-structured Parzen Estimator (TPE) optimization algorithm.
As a final configuration, we choose the set of hyperparameters, corresponding to the smallest loss on the validation set.

assistant: text: We propose a new DNN architecture for deep learning on tabular data