Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? · HackerPapers discussions

Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? — mentioned by ajtulloch

Tue, 01 Jul 2014 15:18:09 GMT

One technique Prof Gower's mentions (the phenomenon of the concentration of measure) has many applications in machine learning/statistics (which may be of more interest to HN readers than some of the probabilistic combinatorics mentioned in the paper). A few (non-rigourous) examples in ML/statistics are: - The use of the LASSO estimator [0] (minimizer of |Y - β X|₂ + λ |β|₁) for variable selection is justified by Candes & Tao's 2005 results [1, 2] that show, under fairly general conditions, that this estimate exactly recovers the true set of active variables. A key step in the proof is…

Paper and discussion history. A mention is not an endorsement.

Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? — mentioned by ajtulloch

Thu, 21 Nov 2013 00:56:00 GMT

My take on this is somewhat different - my background is pure mathematics in my undergraduate degree, then applied machine learning at Facebook (ads optimization) and GS (algo trading), and currently at graduate school in ML. My take is that 'applying' machine learning is much easier than one would think, but 'understanding' machine learning is _much_ harder than one would think. I imagine most people start with an application - that's why they're interested in the first place. Applying an ML algorithm to a given dataset is trivial - only a few lines in R/Python with scikits-learn, etc.…

Paper and discussion history. A mention is not an endorsement.