Understanding Algorithms For Big Data Compsci 229r Lecture 5
Exploring Algorithms For Big Data Compsci 229r Lecture 5 reveals several interesting facts. Analysis of ℓp estimation
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 5
- CountSketch, ℓ0 sampling, graph sketching.
- CountMin sketch, point query,
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Learning from experts, multiplicative weights.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
Hashing: cuckoo hashing analysis, power of two choices. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Amnesic dynamic programming (approximate distance to monotonicity).
Stay tuned for more updates related to Algorithms For Big Data Compsci 229r Lecture 5.