You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms.

Cited Abstract(s):
[1]: Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned web-search ranking - a domain notorious for very large data sets. In this paper, we propose a novel method for parallelizing the training of GBRT. Our technique parallelizes the construction of the individual regression trees and operates using the master-worker paradigm as follows. The data are partitioned among the workers. At each iteration, the worker summarizes its data-partition using histograms. The master processor uses these to build one layer of a regression tree, and then sends this layer to the workers, allowing the workers to build histograms for the next layer. Our algorithm carefully orchestrates overlap between communication and computation to achieve good performance. Since this approach is based on data partitioning, and requires a small amount of communication, it generalizes to distributed and shared memory machines, as well as clouds. We present experimental results on both shared memory machines and clusters for two large scale web search ranking data sets. We demonstrate that the loss in accuracy induced due to the histogram approximation in the regression tree creation can be compensated for through slightly deeper trees. As a result, we see no significant loss in accuracy on the Yahoo data sets and a very small reduction in accuracy for the Microsoft LETOR data. In addition, on shared memory machines, we obtain almost perfect linear speed-up with up to about 48 cores on the large data sets. On distributed memory machines, we get a speedup of 25 with 32 processors. Due to data partitioning our approach can scale to even larger data sets, on which one can reasonably expect even higher speedups.

[2]: Recent studies have shown that boosting provides excellent predictive performance across a wide variety of tasks. In Learning-to-rank, boosted models such as RankBoost and LambdaMART have been shown to be among the best performing learning methods based on evaluations on public data sets. In this paper, we show how the combination of bagging as a variance reduction technique and boosting as a bias reduction technique can result in very high precision and low variance ranking models. We perform thousands of parameter tuning experiments for LambdaMART to achieve a high precision boosting model. Then we show that a bagged ensemble of such LambdaMART boosted models results in higher accuracy ranking models while also reducing variance as much as 50 . We report our results on three public learning-to-rank data sets using four metrics. Bagged LamdbaMART outperforms all previously reported results on ten of the twelve comparisons, and bagged LambdaMART outperforms non-bagged LambdaMART on all twelve comparisons. For example, wrapping bagging around LambdaMART increases NDCG@1 from 0.4137 to 0.4200 on the MQ2007 data set; the best prior results in the literature for this data set is 0.4134 by RankBoost.

[3]: LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world ranking problems: for example an ensemble of LambdaMART rankers won Track 1 of the 2010 Yahoo! Learning To Rank Challenge. The details of these algorithms are spread across several papers and reports, and so here we give a self-contained, detailed and complete description of them.

[4]: 

[5]: Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank.

[6]: As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this paper, we propose an algorithm, the Greedy Miser, that incorporates the feature extraction cost during training to explicitly minimize the cpu-time during testing. The algorithm is a straightforward extension of stage-wise regression and is equally suitable for regression or multi-class classification. Compared to prior work, it is significantly more cost-effective and scales to larger data sets.
Beyond processor architectures, the other area of relevant work is the vast literature on learning to rank [5] , application of machine learning techniques to document ranking in search. Our work uses gradient-boosted regression trees (GBRTs) [3] [1] [2] , a state-of-the-art ensemble method. The focus of most learning-to-rank research is on learning effective models, without considering efficiency, although there is an emerging thread of work that attempts to better balance both factors [4] [6] . In contrast, we focus exclusively on runtime ranking performance, assuming a model that has already been trained (by other means).