Introduction to Algorithms For Big Data Compsci 229r Lecture 12
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 12. Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
Algorithms For Big Data Compsci 229r Lecture 12 Comprehensive Overview
Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. ORS theorem (distributional JL implies Gordon's theorem), sparse JL. Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma).
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 12
- Matrix completion.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- To follow along with the course, visit the course website: https://web.stanford.edu/class/archive/cs/cs109/cs109.1232/ Chris Piech ...
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 12.