Supervised Machine Learning Under Test-Time Resource Constraints: A Trade-off Between Accuracy and Cost

The past decade has witnessed how the field of machine learning has established itself as a necessary component in several multi-billion-dollar industries. The real-world industrial setting introduces an interesting new problem to machine learning research: computational resources must be budgeted and cost must be strictly accounted for during test-time. A typical problem is that if an application consumes x additional units of cost during test-time, but will improve accuracy by y percent, should the additional x resources be allocated? The core of this problem is a trade-off between accuracy and cost. In this thesis, we examine components of test-time cost, and develop different strategies to manage this trade-off. We first investigate test-time cost and discover that it typically consists of two parts: feature extraction cost and classifier evaluation cost. The former reflects the computational efforts of transforming data instances to feature vectors, and could be highly variable when features are heterogeneous. The latter reflects the effort of evaluating a classifier, which could be substantial, in particular nonparametric algorithms. We then propose three strategies to explicitly trade-off accuracy and the two components of test-time cost during classifier training. To budget the feature extraction cost, we first introduce two algorithms: GreedyMiser and Anytime Representation Learning (AFR). GreedyMiser employs a strategy that incorporates the extraction cost information during classifier training to explicitly minimize the test-time cost. AFR extends GreedyMiser to learn a cost-sensitive feature representation rather than a classifier, and turns traditional Support Vector Machines (SVM) into test- time cost-sensitive anytime classifiers. GreedyMiser and AFR are evaluated on two real-world data sets from two different application domains, and both achieve record performance. We then introduce Cost Sensitive Tree of Classifiers (CSTC) and Cost Sensitive Cascade of Classifiers (CSCC), which share a common strategy that trades-off the accuracy and the amortized test-time cost. CSTC introduces a tree structure and directs test inputs along different tree traversal paths, each is optimized for a specific sub-partition of the input space, extracting different, specialized subsets of features. CSCC extends CSTC and builds a linear cascade, instead of a tree, to cope with class-imbalanced binary classification tasks. Since both CSTC and CSCC extract different features for different inputs, the amortized test-time cost is greatly reduced while maintaining high accuracy. Both approaches out-perform the current state-of-the-art on real-world data sets. To trade-off accuracy and high classifier evaluation cost of nonparametric classifiers, we propose a model compression strategy and develop Compressed Vector Machines (CVM). CVM focuses on the nonparametric kernel Support Vector Machines (SVM), whose test-time evaluation cost is typically substantial when learned from large training sets. CVM is a post-processing algorithm which compresses the learned SVM model by reducing and optimizing support vectors. On several benchmark data sets, CVM maintains high test accuracy while reducing the test-time evaluation cost by several orders of magnitude

Learning in the Real World: Constraints on Cost, Space, and Privacy

The sheer demand for machine learning in fields as varied as: healthcare, web-search ranking, factory automation, collision prediction, spam filtering, and many others, frequently outpaces the intended use-case of machine learning models. In fact, a growing number of companies hire machine learning researchers to rectify this very problem: to tailor and/or design new state-of-the-art models to the setting at hand. However, we can generalize a large set of the machine learning problems encountered in practical settings into three categories: cost, space, and privacy. The first category (cost) considers problems that need to balance the accuracy of a machine learning model with the cost required to evaluate it. These include problems in web-search, where results need to be delivered to a user in under a second and be as accurate as possible. The second category (space) collects problems that require running machine learning algorithms on low-memory computing devices. For instance, in search-and-rescue operations we may opt to use many small unmanned aerial vehicles (UAVs) equipped with machine learning algorithms for object detection to find a desired search target. These algorithms should be small to fit within the physical memory limits of the UAV (and be energy efficient) while reliably detecting objects. The third category (privacy) considers problems where one wishes to run machine learning algorithms on sensitive data. It has been shown that seemingly innocuous analyses on such data can be exploited to reveal data individuals would prefer to keep private. Thus, nearly any algorithm that runs on patient or economic data falls under this set of problems. We devise solutions for each of these problem categories including (i) a fast tree-based model for explicitly trading off accuracy and model evaluation time, (ii) a compression method for the k-nearest neighbor classifier, and (iii) a private causal inference algorithm that protects sensitive data

Using classifier cascades for scalable e-mail classification

In many real-world scenarios, we must make judgments in the presence of computational constraints. One common computational constraint arises when the features used to make a judgment each have differing acquisition costs, but there is a fixed total budget for a set of judgments. Particularly when there are a large number of classifications that must be made in a real-time, an intelligent strategy for optimizing accuracy versus computational costs is essential. E-mail classification is an area where accurate and timely results require such a trade-off. We identify two scenarios where intelligent feature acquisition can improve classifier performance. In granular classification we seek to classify e-mails with increasingly specific labels structured in a hierarchy, where each level of the hierarchy requires a different trade-off between cost and accuracy. In load-sensitive classification, we classify a set of instances within an arbitrary total budget for acquiring features. Our method, Adaptive Classifier Cascades (ACC), designs a policy to combine a series of base classifiers with increasing computational costs given a desired trade-off between cost and accuracy. Using this method, we learn a relationship between feature costs and label hierarchies, for granular classification and cost budgets, for load-sensitive classification. We evaluate our method on real-world e-mail datasets with realistic estimates of feature acquisition cost, and we demonstrate superior results when compared to baseline classifiers that do not have a granular, cost-sensitive feature acquisition policy. 1