Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget. All of our experimental results are reproducible via a one-line script at https://github.com/ThrunGroup/FastForest.

Maximum Inner Product Search (MIPS) is a popular problem in the machine learning literature due to its applicability in a wide array of applications, such as recommender systems. In high-dimensional settings, however, MIPS queries can become computationally expensive as most existing solutions do not scale well with data dimensionality. In this work, we present a state-of-the-art algorithm for the MIPS problem in high dimensions, dubbed BanditMIPS. BanditMIPS is a randomized algorithm that borrows techniques from multi-armed bandits to reduce the MIPS problem to a best-arm identification problem. BanditMIPS reduces the complexity of state-of-the-art algorithms from $O(\sqrt{d})$ to $O(\text{log}d)$, where $d$ is the dimension of the problem data vectors. On high-dimensional real-world datasets, BanditMIPS runs approximately 12 times faster than existing approaches and returns the same solution. BanditMIPS requires no preprocessing of the data and includes a hyperparameter that practitioners may use to trade off accuracy and runtime. We also propose a variant of our algorithm, named BanditMIPS-$\alpha$, which employs non-uniform sampling across the data dimensions to provide further speedups.

决策森林（森林），尤其是随机森林和梯度促进树木，与许多监督学习场景中的其他方法相比，已经证明了最先进的准确性。尤其是，森林在表格数据中占主导地位，即当特征空间非结构化时，因此信号是特征指数置换的不变性。然而，在存在于多种多样（例如图像，文本和语音）深网（网络）（特别是卷积深网（Convnets））上的结构化数据中，倾向于优于森林。我们猜想至少部分原因是网络的输入不仅仅是特征幅度，也是其索引。相反，天真的森林实施未能明确考虑特征指数。最近提出的森林方法表明，对于每个节点，森林从某些特定分布中隐式采样一个随机矩阵。这些森林像某些类别的网络一样，通过将特征空间划分为对应于线性函数的凸多物体来学习。我们以这种方法为基础，并表明人们可以以多种感知方式选择分布来纳入特征区域。我们在数据上活在三个不同的流形上的数据上证明了经验性能：圆环，图像和时间序列。此外，我们证明了其在多元模拟环境中的强度，并且在预测癫痫患者的手术结果方面也表现出了优越性，并从非运动脑区域的原始立体定向EEG数据中预测运动方向。在所有模拟和真实数据中，歧管随机森林（MORF）算法的表现优于忽略特征空间结构并挑战Convnets的性能。此外，MORF运行迅速，并保持解释性和理论上的理由。

从机器学习模型中删除指定的培训数据子集的影响可能需要解决隐私，公平和数据质量等问题。删除子集后剩余数据从头开始对模型进行重新审查是有效但通常是不可行的，因为其计算费用。因此，在过去的几年中，已经看到了几种有效拆除的新方法，形成了“机器学习”领域，但是，到目前为止，出版的文献的许多方面都是不同的，缺乏共识。在本文中，我们总结并比较了七个最先进的机器学习算法，合并对现场中使用的核心概念的定义，调和不同的方法来评估算法，并讨论与在实践中应用机器相关的问题。

This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced.

决策树学习是机器学习中广泛使用的方法，在需要简洁明了的模型的应用中受到青睐。传统上，启发式方法用于快速生产具有相当高准确性的模型。然而，一个普遍的批评是，从精度和大小方面，所产生的树可能不一定是数据的最佳表示。近年来，这激发了最佳分类树算法的发展，这些算法与执行一系列本地最佳决策的启发式方法相比，在全球范围内优化决策树。我们遵循这一工作线，并提供了一种基于动态编程和搜索的最佳分类树的新颖算法。我们的算法支持对树的深度和节点数量的约束。我们方法的成功归因于一系列专门技术，这些技术利用了分类树独有的属性。传统上，最佳分类树的算法受到了高运行时的困扰和有限的可伸缩性，但我们在一项详细的实验研究中表明，我们的方法仅使用最先进的时间所需的时间，并且可以处理数十个数据集的数据集在数千个实例中，提供了几个数量级的改进，并特别有助于实现最佳决策树的实现。

We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must select a batch of arms, each characterized by its own context, that would allow it to both maximize reward and maintain an accurate (ideally unbiased) population estimate of the reward. This setting is inherent to many public and private sector applications and often requires handling delayed feedback, small data, and distribution shifts. We demonstrate its importance on real data from the United States Internal Revenue Service (IRS). The IRS performs yearly audits of the tax base. Two of its most important objectives are to identify suspected misreporting and to estimate the "tax gap" -- the global difference between the amount paid and true amount owed. Based on a unique collaboration with the IRS, we cast these two processes as a unified optimize-and-estimate structured bandit. We analyze optimize-and-estimate approaches to the IRS problem and propose a novel mechanism for unbiased population estimation that achieves rewards comparable to baseline approaches. This approach has the potential to improve audit efficacy, while maintaining policy-relevant estimates of the tax gap. This has important social consequences given that the current tax gap is estimated at nearly half a trillion dollars. We suggest that this problem setting is fertile ground for further research and we highlight its interesting challenges. The results of this and related research are currently being incorporated into the continual improvement of the IRS audit selection methods.

我们查看模型可解释性的特定方面：模型通常需要限制在大小上才能被认为是可解释的，例如，深度5的决策树比深度50中的一个更容易解释。但是，较小的模型也倾向于高偏见。这表明可解释性和准确性之间的权衡。我们提出了一种模型不可知论技术，以最大程度地减少这种权衡。我们的策略是首先学习甲骨文，这是培训数据上高度准确的概率模型。 Oracle预测的不确定性用于学习培训数据的抽样分布。然后，对使用此分布获得的数据样本进行了可解释的模型，通常会导致精确度明显更高。我们将抽样策略作为优化问题。我们的解决方案1具有以下关键的有利属性：（1）它使用固定数量的七个优化变量，而与数据的维度（2）无关，它是模型不可知的 - 因为可解释的模型和甲骨文都可能属于任意性模型家族（3）它具有模型大小的灵活概念，并且可以容纳向量大小（4）它是一个框架，使其能够从优化领域的进度中受益。我们还提出了以下有趣的观察结果：（a）通常，小型模型大小的最佳训练分布与测试分布不同； （b）即使可解释的模型和甲骨文来自高度截然不同的模型家族，也存在这种效果：我们通过使用封闭的复发单位网络作为甲骨文来提高决策树的序列分类精度，从而在文本分类任务上显示此效果。使用字符n-grams； （c）对于模型，我们的技术可用于确定给定样本量的最佳训练样本。

Data depth, introduced by Tukey (1975), is an important tool in data science, robust statistics, and computational geometry. One chief barrier to its broader practical utility is that many common measures of depth are computationally intensive, requiring on the order of $n^d$ operations to exactly compute the depth of a single point within a data set of $n$ points in $d$-dimensional space. Often however, we are not directly interested in the absolute depths of the points, but rather in their \textit{relative ordering}. For example, we may want to find the most central point in a data set (a generalized median), or to identify and remove all outliers (points on the fringe of the data set with low depth). With this observation, we develop a novel and instance-adaptive algorithm for adaptive data depth computation by reducing the problem of exactly computing $n$ depths to an $n$-armed stochastic multi-armed bandit problem which we can efficiently solve. We focus our exposition on simplicial depth, developed by \citet{liu1990notion}, which has emerged as a promising notion of depth due to its interpretability and asymptotic properties. We provide general instance-dependent theoretical guarantees for our proposed algorithms, which readily extend to many other common measures of data depth including majority depth, Oja depth, and likelihood depth. When specialized to the case where the gaps in the data follow a power law distribution with parameter $\alpha<2$, we show that we can reduce the complexity of identifying the deepest point in the data set (the simplicial median) from $O(n^d)$ to $\tilde{O}(n^{d-(d-1)\alpha/2})$, where $\tilde{O}$ suppresses logarithmic factors. We corroborate our theoretical results with numerical experiments on synthetic data, showing the practical utility of our proposed methods.

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration nonstochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, Hyperband, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that Hyperband can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.

大多数机器学习算法由一个或多个超参数配置，必须仔细选择并且通常会影响性能。为避免耗时和不可递销的手动试验和错误过程来查找性能良好的超参数配置，可以采用各种自动超参数优化（HPO）方法，例如，基于监督机器学习的重新采样误差估计。本文介绍了HPO后，本文审查了重要的HPO方法，如网格或随机搜索，进化算法，贝叶斯优化，超带和赛车。它给出了关于进行HPO的重要选择的实用建议，包括HPO算法本身，性能评估，如何将HPO与ML管道，运行时改进和并行化结合起来。这项工作伴随着附录，其中包含关于R和Python的特定软件包的信息，以及用于特定学习算法的信息和推荐的超参数搜索空间。我们还提供笔记本电脑，这些笔记本展示了这项工作的概念作为补充文件。

普通交叉验证（CV）等方法，如k倍交叉验证或Monte-Carlo交叉验证估计学习者的预测性能，通过重复在给定数据的大部分数据和对剩余数据上测试的大部分中进行训练。这些技术有两个主要缺点。首先，它们可以在大型数据集上不必要地慢。其次，除了估计最终性能之外，它们几乎没有进入验证算法的学习过程中的见解。在本文中，我们提出了一种基于学习曲线（LCCV）的验证的新方法。 LCCV迭代地增加用于训练的实例数量而不是创建火车测试分裂。在模型选择的背景下，它丢弃了不太可能成为竞争的模型。我们在从自动化基准测试的67个数据集上运行大规模的实验，并经验显示使用LCCV超过90％的案例，导致使用5/10倍的CV相似的性能（最多1.5％）。然而，它平均产生超过20％的大量运行时间减少。此外，它提供了重要的见解，例如允许评估获取更多数据的益处。这些结果与Automl领域的其他进步正交。

Tree Ensembles可以非常适合黑盒优化任务，例如算法调整和神经体系结构搜索，因为它们在几乎没有手动调整的情况下实现了良好的预测性能，自然可以处理离散的功能空间，并且对培训中的异常值相对不敏感数据。在使用树的组合进行黑盒优化方面面临的两个众所周知的挑战是（i）有效地量化模型的不确定性，以进行探索，以及（ii）优化在零件的恒定采集函数上。为了同时解决这两个点，我们建议在获得模型方差估计之前使用树的内核解释为高斯过程，并为采集函数开发兼容的优化公式。后者进一步使我们能够通过考虑工程设置中的域知识和建模搜索空间对称性，例如神经体系结构搜索中的层次结构关系，从而无缝整合已知约束，以提高采样效率。我们的框架以及最先进的方法以及对连续/离散功能的不受限制的黑框优化，并且优于结合混合变量特征空间和已知输入约束的问题的竞争方法。

Many scientific and engineering challenges-ranging from personalized medicine to customized marketing recommendations-require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect, and have an asymptotically Gaussian and centered sampling distribution. We also discuss a practical method for constructing asymptotic confidence intervals for the true treatment effect that are centered at the causal forest estimates. Our theoretical results rely on a generic Gaussian theory for a large family of random forest algorithms. To our knowledge, this is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference. In experiments, we find causal forests to be substantially more powerful than classical methods based on nearest-neighbor matching, especially in the presence of irrelevant covariates.

Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations such as XGBoost and pGBRT. Although many engineering optimizations have been adopted in these implementations, the efficiency and scalability are still unsatisfactory when the feature dimension is high and data size is large. A major reason is that for each feature, they need to scan all the data instances to estimate the information gain of all possible split points, which is very time consuming. To tackle this problem, we propose two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). With GOSS, we exclude a significant proportion of data instances with small gradients, and only use the rest to estimate the information gain. We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size. With EFB, we bundle mutually exclusive features (i.e., they rarely take nonzero values simultaneously), to reduce the number of features. We prove that finding the optimal bundling of exclusive features is NP-hard, but a greedy algorithm can achieve quite good approximation ratio (and thus can effectively reduce the number of features without hurting the accuracy of split point determination by much). We call our new GBDT implementation with GOSS and EFB LightGBM. Our experiments on multiple public datasets show that, LightGBM speeds up the training process of conventional GBDT by up to over 20 times while achieving almost the same accuracy.

稀疏决策树优化是AI自成立以来的最基本问题之一，并且是可解释机器学习核心的挑战。稀疏的决策树优化是计算地的艰难，尽管自1960年代以来稳定的努力，但在过去几年中才突破问题，主要是在找到最佳稀疏决策树的问题上。然而，目前最先进的算法通常需要不切实际的计算时间和内存，以找到一些真实世界数据集的最佳或近最优树，特别是那些具有多个连续值的那些。鉴于这些决策树优化问题的搜索空间是大规模的，我们可以实际上希望找到一个稀疏的决策树，用黑盒机学习模型的准确性竞争吗？我们通过智能猜测策略来解决这个问题，可以应用于基于任何最优分支和绑定的决策树算法。我们表明，通过使用这些猜测，我们可以通过多个数量级来减少运行时间，同时提供所得树木可以偏离黑匣子的准确性和表现力的界限。我们的方法可以猜测如何在最佳决策树错误的持续功能，树的大小和下限上进行换算。我们的实验表明，在许多情况下，我们可以迅速构建符合黑匣子型号精度的稀疏决策树。总结：当您在优化时遇到困难时，就猜测。

近年来，随着传感器和智能设备的广泛传播，物联网（IoT）系统的数据生成速度已大大增加。在物联网系统中，必须经常处理，转换和分析大量数据，以实现各种物联网服务和功能。机器学习（ML）方法已显示出其物联网数据分析的能力。但是，将ML模型应用于物联网数据分析任务仍然面临许多困难和挑战，特别是有效的模型选择，设计/调整和更新，这给经验丰富的数据科学家带来了巨大的需求。此外，物联网数据的动态性质可能引入概念漂移问题，从而导致模型性能降解。为了减少人类的努力，自动化机器学习（AUTOML）已成为一个流行的领域，旨在自动选择，构建，调整和更新机器学习模型，以在指定任务上实现最佳性能。在本文中，我们对Automl区域中模型选择，调整和更新过程中的现有方法进行了审查，以识别和总结将ML算法应用于IoT数据分析的每个步骤的最佳解决方案。为了证明我们的发现并帮助工业用户和研究人员更好地实施汽车方法，在这项工作中提出了将汽车应用于IoT异常检测问题的案例研究。最后，我们讨论并分类了该领域的挑战和研究方向。

缺乏标记的培训数据是许多应用程序中机器学习的瓶颈。为了解决瓶颈，一个有希望的方向是数据编程方法，该方法汇总了弱监督信号的不同来源，以轻松生成标记的数据。数据编程使用标签功能（LF）编码每个弱监督源，这是一个预测嘈杂标签的用户提供的程序。生成的标签的质量取决于标签聚合模型，该模型汇总了所有LFS的所有嘈杂标签以推断地面真相标签。现有的标签聚合方法通常依赖于各种假设，并且在整个数据集中都不强大，因为我们将在经验上显示。我们首次提供了一种分析标签聚合方法，该方法是最小化假设的，并且在最小化某种形式的平均预测误差方面是最佳的。由于分析形式的复杂性是指数级的，因此我们训练一个学会成为分析方法的模型。经过训练后，该模型可用于任何看不见的数据集，该模型可以在线性时间内单个正向通行证中每个数据集的地面真相标签。我们显示该模型可以使用合成生成的数据进行训练，并为模型设计有效的体系结构。在14个现实世界数据集上，我们的模型在准确性（平均为3.5点）和效率（平均降低六倍）方面大大优于现有方法。

无论是在功能选择的领域还是可解释的AI领域，都有基于其重要性的“排名”功能的愿望。然后可以将这种功能重要的排名用于：（1）减少数据集大小或（2）解释机器学习模型。但是，在文献中，这种特征排名没有以系统的，一致的方式评估。许多论文都有不同的方式来争论哪些具有重要性排名最佳的特征。本文通过提出一种新的评估方法来填补这一空白。通过使用合成数据集，可以事先知道特征重要性得分，从而可以进行更系统的评估。为了促进使用新方法的大规模实验，在Python建造了一个名为FSEVAL的基准测定框架。该框架允许并行运行实验，并在HPC系统上的计算机上分布。通过与名为“权重和偏见”的在线平台集成，可以在实时仪表板上进行交互探索图表。该软件作为开源软件发布，并在PYPI平台上以包裹发行。该研究结束时，探索了一个这样的大规模实验，以在许多方面找到参与算法的优势和劣势。

算法配置（AC）与对参数化算法最合适的参数配置的自动搜索有关。目前，文献中提出了各种各样的交流问题变体和方法。现有评论没有考虑到AC问题的所有衍生物，也没有提供完整的分类计划。为此，我们引入分类法以分别描述配置方法的交流问题和特征。我们回顾了分类法的镜头中现有的AC文献，概述相关的配置方法的设计选择，对比方法和问题变体相互对立，并描述行业中的AC状态。最后，我们的评论为研究人员和从业人员提供了AC领域的未来研究方向。