Given ubiquitous graph data such as the Web and social networks, proximity search on
graphs has been an active research topic. The task boils down to measuring the proximity …

In the'Big Data'era, many real-world applications like search involve the ranking problem for
a large number of items. It is important to obtain effective ranking results and at the same …

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 …

Complex machine learning models are now an integral part of modern, large-scale retrieval
systems. However, collection size growth continues to outpace advances in efficiency …

Ensemble learning–the application of multiple learning models on the same task–is a
common technique in multiple domains. While employing multiple models enables reaching …

Abstract Machine learning algorithms are increasingly used in large-scale industrial settings.
Here, the operational cost during test-time has to be taken into account when an algorithm is …

Reducing excessive costs in feature acquisition and model evaluation has been a long-
standing challenge in learning-to-rank systems. A cascaded ranking architecture turns …

Vision-based player detection and classification are important in sports applications.
Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as …

In real-world search, recommendation, and advertising systems, the multi-stage ranking
architecture is commonly adopted. Such architecture usually consists of matching, pre …

Abstract Systems for performing ML inference are widely deployed today. However, they
typically use techniques designed for conventional data serving workloads, missing critical …