Sample topics: low-distortion embeddings of finite metrics into L_1, L_p, and distributions of trees; dimensionality reduction; volume-respecting embeddings; applications to graph partitioning, online algorithms, network design, and nearest neighbor search. Prerequisites: CS261 or comparable mathematical maturity.
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