Understanding Algorithms For Big Data Compsci 229r Lecture 7
Exploring Algorithms For Big Data Compsci 229r Lecture 7 reveals several interesting facts. CountSketch, ℓ0 sampling, graph sketching.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 7
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- CountMin sketch, point query,
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 7
Amnesic dynamic programming (approximate distance to monotonicity). Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Competitive paging, cache-oblivious
Analysis of ℓp estimation
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