Insights: machine learning

Two Sigma researchers highlight several papers from ICML 2018 that they found particularly novel, practical, or otherwise compelling.

Two Sigma researchers provide an overview of some of the most interesting lectures and sessions at JSM 2018, and highlight some of the most important challenges statisticians face going forward.

An interview with Andy Pavlo, an assistant professor of databaseology in the Computer Science Department at CMU to discuss his current research and outlook on the future of database management.

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.

Two Sigma researchers share highlights from NIPS 2017.

An overview of best practices derived from building a machine-learning based starter bot for Halite, Two Sigma's public artificial intelligence programming challenge.

A senior Two Sigma researcher provides an overview of some of the most interesting Deep Learning research from ICML 2017.

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.

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).