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.
Two Sigma researchers discuss notable advances in deep learning, optimization algorithms, Bayesian techniques, and time-series analysis presented at 2016's Conference on Neural Information Processing Systems (NIPS).
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.
Berkeley’s Professor David E. Culler discusses the future of data science, the “Berkeley view” of the field, and the biggest challenges for data scientists today.
A presentation on fundamental questions in algorithmic data science, a discipline at the border of computer science and statistics.
A Two Sigma research scientist provides an overview of some of the most interesting research presented at ICML 2016.
ABRA is a suite of algorithms to compute and maintain probabilistically-guaranteed, high-quality, approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs.
The authors present an algorithm to help detect new information and events in a network by computing an optimal probing schedule that minimizes the average novelty of undetected items.