Exploring Algorithms For Big Data Compsci 229r Lecture 3
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 3.
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 3
Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Hashing: load balancing, k-wise independence, chaining, linear probing. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Analysis of ℓp estimation
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 3.