Understanding Algorithms For Big Data Compsci 229r Lecture 7
Exploring Algorithms For Big Data Compsci 229r Lecture 7 reveals several interesting facts. CountSketch, â„“0 sampling, graph sketching.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 7
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Splay trees.
- Matrix completion.
- Analysis of â„“p estimation
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 7
Amnesic dynamic programming (approximate distance to monotonicity). CountMin sketch, point query, Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
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