Boström, Henrik

Block-distributed Gradient Boosted Trees Machine Learning

The Gradient Boosted Tree (GBT) algorithm is one of the most popular machine learning algorithms used in production, for tasks that include Click-Through Rate (CTR) prediction and learning-to-rank. To deal with the massive datasets available today, many distributed GBT methods have been proposed. However, they all assume a row-distributed dataset, addressing scalability only with respect to the number of data points and not the number of features, and increasing communication cost for high-dimensional data. In order to allow for scalability across both the data point and feature dimensions, and reduce communication cost, we propose block-distributed GBTs. We achieve communication efficiency by making full use of the data sparsity and adapting the Quickscorer algorithm to the block-distributed setting. We evaluate our approach using datasets with millions of features, and demonstrate that we are able to achieve multiple orders of magnitude reduction in communication cost for sparse data, with no loss in accuracy, while providing a more scalable design. As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.

Clustering with Confidence: Finding Clusters with Statistical Guarantees Machine Learning

Clustering is a widely used unsupervised learning method for finding structure in the data. However, the resulting clusters are typically presented without any guarantees on their robustness; slightly changing the used data sample or re-running a clustering algorithm involving some stochastic component may lead to completely different clusters. There is, hence, a need for techniques that can quantify the instability of the generated clusters. In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which correspond to clusters where the co-occurrence probability of each data item within a cluster is at least $1 - \alpha$. We demonstrate how solving the core clustering problem is linked to finding the largest maximal cliques in a graph. We show that the method can be used with both clustering and classification algorithms. The proposed method is tested on both simulated and real datasets. The results show that the obtained clusters indeed meet the guarantees on robustness.

Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins

AAAI Conferences

The standard approach of learning decision trees from histogram data is to treat the bins as independent variables. However, as the underlying dependencies among the bins might not be completely exploited by this approach, an algorithm has been proposed for learning decision trees from histogram data by considering all bins simultaneously while partitioning examples at each node of the tree. Although the algorithm has been demonstrated to improve predictive performance, its computational complexity has turned out to be a major bottleneck, in particular for histograms with a large number of bins. In this paper, we propose instead a sliding window approach to select subsets of the bins to be considered simultaneously while partitioning examples. This significantly reduces the number of possible splits to consider, allowing for substantially larger histograms to be handled. We also propose to evaluate the original bins independently, in addition to evaluating the subsets of bins when performing splits. This ensures that the information obtained by treating bins simultaneously is an additional gain compared to what is considered by the standard approach. Results of experiments on applying the new algorithm to both synthetic and real world datasets demonstrate positive results in terms of predictive performance without excessive computational cost.