MiSoSouP is a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different interestingness measures, from a random sample of a transactional dataset.
In the machine learning research community, it is generally believed that there is a tension between memorization and generalization. This paper examines the extent to which this tension exists, by exploring whether it is possible to generalize by memorizing alone.
A senior Two Sigma researcher provides an overview of some of the most interesting Deep Learning research from ICML 2017.
A group of Two Sigma statisticians highlight a selection of interesting talks and presentations from the 2017 Joint Statistical Meeting.
Two Sigma Co-founder and Co-chairman David Siegel offers his views on these topics and more at Bloomberg’s Sooner Than You Think conference.
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.