Rethinking algorithms in Data Science: Scaling up optimization using non-convexity, provably

With the quantity of generated data ever-increasing in most research areas, conventional data analytics run into solid computational, storage, and communication bottlenecks. These obstacles force practitioners to often use algorithmic heuristics, in an attempt to convert data into useful information, fast. It is necessary to rethink the algorithmic design, and devise smarter and provable methods in order to flexibly balance the trade-offs between solution accuracy, efficiency, and data interpretability.In this talk, I will focus on the problem of low rank matrix inference in large-scale settings. Such problems appear in fundamental applications such as structured inference, recommendation systems and multi-label classification problems. I will introduce a novel theoretical framework for analyzing the performance of non-convex first-order methods, often used as heuristics in practice. These methods lead to computational gains over classic…


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