Understanding Algorithms For Big Data Compsci 229r Lecture 4
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 4, you have come to the right place. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 4
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Hashing: cuckoo hashing analysis, power of two choices.
- Competitive paging, cache-oblivious
- Amnesic dynamic programming (approximate distance to monotonicity).
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 4
Analysis of ℓp estimation Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... Symmetrization, hashing: linear probing (5-wise indep.), bloom filters, cuckoo hashing, bloomier filters.
Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
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