Non-convex methods for high-dimensional regression with noisy and missing data

Noisy and missing data are prevalent in many real-world statistical estimation problems. Popular techniques for handling non-idealities in data, such as imputation and expectation-maximization, are often difficult to analyze theoretically and/or terminate in local optima of non-convex functions -- these problems are only exacerbated in high-dimensional settings. We present new methods for obtaining high-dimensional regression estimators in the presence of corrupted data, and provide theoretical guarantees for the statistical consistency of our methods.

Programming for Everyone: From Solvers to Solver-Aided Languages and Beyond


We live in a software-driven world. Software helps us communicate and collaborate; create art and music; and make discoveries in biological, physical, and social sciences. Yet the growing demand for new software, to solve new kinds of problems, remains largely unmet. Because programming is still hard, developer productivity is limited, and so is end-users' ability to program on their own.


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