Modern large-scale ML applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information such as stochastic gradients among different workers. In this paper, to reduce the communication cost, we propose a convex optimization formulation to minimize the coding length of stochastic gradients.
The authors suggest a general oracle-based framework that captures different parallel stochastic optimization settings described by a dependency graph, and derive generic lower bounds in terms of this graph, as well as lower bounds for several specific parallel optimization settings. They highlight gaps between lower and upper bounds on the oracle complexity, and cases where the “natural” algorithms are not known to be optimal.
Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression (SLR) have a wide range of applications and have attracted attention as canonical examples of statistical problems in high dimension. A variety of algorithms have been proposed for both SPCA and SLR, but an explicit connection between the two had not been made. This paper shows how to efficiently transform a black-box solver for SLR into an algorithm for SPCA.
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.
This presentation surveys the principles needed for a successful AI programming competition and describes the architecture of the game environment, particularly the support that GCP provided for the support of 12 million game executions written in over 20 programming languages.
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.