We give new results for problems in computational and statistical machine
learning using tools from high-dimensional geometry and probability.
We break up our treatment into two parts. In Part I, we focus on
computational considerations in optimization. Specifically, we give new
algorithms for approximating convex polytopes in a stream, sparsification and
robust least squares regression, and dueling optimization.
In Part II, we give new statistical guarantees for data science problems. In
particular, we formulate a new model in which we analyze statistical properties
of backdoor data poisoning attacks, and we study the robustness of graph
clustering algorithms to “helpful” misspecification.

Questo articolo esplora i giri e le loro implicazioni.

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