Exploring Algorithms For Big Data Compsci 229r Lecture 3
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 3.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Hashing: load balancing, k-wise independence, chaining, linear probing.
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
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. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 3.
