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  • Data Science

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.

  • Data Science

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.