Given an ensemble of randomized regression trees, it is possible to
restructure them as a collection of multilayered neural networks with
particular connection weights. Following this principle, we reformulate the
random forest method of Breiman (2001) into a neural network setting, and in
turn propose two new hybrid procedures that we call neural random forests. Both
predictors exploit prior knowledge of regression trees for their architecture,
have less parameters to tune than standard networks, and less restrictions on
the geometry of the decision boundaries than trees. Consistency results are
proved, and substantial numerical evidence is provided on both synthetic and
real data sets to assess the excellent performance of our methods in a large
variety of prediction problems.

将随机回归树方法改成了一个新的神经网络模型，称为神经随机森林。 基于随机回归树的架构利用了先验知识并具有更少的参数，较少的限制设计决策路径和优秀的表现使得方法可用于多样的预测问题。

神经随机森林

Neural Random Forests

Many prediction domains, such as ad placement, recommendation, trajectory
prediction, and document summarization, require predicting a set or list of
options. Such lists are often evaluated using submodular reward functions that
measure both quality and diversity. We propose a simple, efficient, and
provably near-optimal approach to optimizing such prediction problems based on
no-regret learning. Our method leverages a surprising result from online
submodular optimization: a single no-regret online learner can compete with an
optimal sequence of predictions. Compared to previous work, which either learn
a sequence of classifiers or rely on stronger assumptions such as
realizability, we ensure both data-efficiency as well as performance guarantees
in the fully agnostic setting. Experiments validate the efficiency and
applicability of the approach on a wide range of problems including manipulator
trajectory optimization, news recommendation and document summarization.

本文介绍了一种基于无悔学习的简单、高效、近似最优化的方法，以优化具有多种选择项的预测问题。实验验证了该方法在各种问题中的高效性和适用性，包括机械臂轨迹优化、新闻推荐和文件摘要。

上下文子模预测学习策略

Learning Policies for Contextual Submodular Prediction

We introduce supervised latent Dirichlet allocation (sLDA), a statistical
model of labelled documents. The model accommodates a variety of response
types. We derive an approximate maximum-likelihood procedure for parameter
estimation, which relies on variational methods to handle intractable posterior
expectations. Prediction problems motivate this research: we use the fitted
model to predict response values for new documents. We test sLDA on two
real-world problems: movie ratings predicted from reviews, and the political
tone of amendments in the U.S. Senate based on the amendment text. We
illustrate the benefits of sLDA versus modern regularized regression, as well
as versus an unsupervised LDA analysis followed by a separate regression.

介绍了一种新的统计模型 —— 监督潜在狄利克雷分配（SLDA），应用变分方法处理难以计算的后验期望，解决回答类型的多样性并预测新文档的回答数值。与现代正则化回归以及无监督 LDA 分析相比，SLDA 有显著优势。