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Responsive and Scalable Real-time Data Analytics

Designing a system that can extract immediate insights from large amounts of data in real-time requires a special way of thinking. This talk presents a “reactive” approach to designing real-time, responsive, and scalable data applications that can continuously compute analytics on-the-fly. It also highlights a case study as an example of reactive design in action.

JSM 2017 Experiences

A group of Two Sigma statisticians highlight a selection of interesting talks and presentations from the 2017 Joint Statistical Meeting.

Rademacher Averages: Theory and Practice

An overview of Rademacher Averages, a fundamental concept from statistical learning theory that can be used to derive uniform sample-dependent bounds to the deviation of samples averages from their expectations.

Graph Summarization with Quality Guarantees

Given a large graph, the authors we aim at producing a concise lossy representation (a summary) that can be stored in main memory and used to approximately answer queries about the original graph much faster than by using the exact representation.