A group of Two Sigma statisticians highlight a selection of interesting talks and presentations from the 2017 Joint Statistical Meeting.
The Vera Institute of Justice (Vera) partnered with with Two Sigma’s Data Clinic, a volunteer-based program that leverages employees’ data science expertise, to uncover the factors contributing to continued jail growth in rural areas.
The authors present TRIÈST, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions.
Speed and repeatability are crucial in machine learning, but the latter is not guaranteed in TensorFlow. A Two Sigma researcher demonstrates a workaround to attain repeatable results.
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 Two Sigma research scientist provides an overview of some of the most interesting research presented at ICML 2016.
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.