Understanding Algorithms For Big Data Compsci 229r Lecture 10
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 10. Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma).
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 10
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Matrix completion.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Titus Brown Random
- ORS theorem (distributional JL implies Gordon's theorem), sparse JL.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 10
Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
Krahmer-Ward proof, Iterative Hard Thresholding.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 10.
