March 2020

Experts on the COVID-19 pandemic seem to think there are three ways out—that is, for life, health, and the economy to return roughly to normal.  Either we get a vaccine good enough that R0 for the world goes below 1, a good enough treatment that people no longer need to be afraid, or we develop a great culture of testing, contract tracing, masks, and isolation. I wish that the federal government were doing much more—it would be great […]

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I’m trying to fund startups/projects helping with COVID-19, because it’s basically the one thing I know how to do that can help.  I think we will soon have enough testing capacity, so now I’d like to start funding more startups working on: Producing a lot of ventilators or masks/gowns very quickly.  This will require a lot of repurposing and creativity but thankfully is an engineering problem not a scientific ones. Screening existing drugs for effectiveness. Novel approaches to […]

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Although I still hope things will go differently, the experts I’ve spoken to think we are likely to face a global tragedy—hundreds of thousands of deaths from Covid-19. I hope that society views this as a warning for the future.  Covid-19 is bad, but only a warm-up.  I think it’s unlikely that this is the worst new pandemic (human-created or otherwise) we’ll see in our lifetimes.  We need to be ready to deal with it much better next […]

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Preliminaries Committee Boosting The committee has an equal weight for every prediction from all models, and it gives little improvement than a single model. Then boosting was built for this problem. Boosting is a technique of combining multiple ‘base’ classifiers to produce a form of the committee that: performances better than any of the base classifiers and each base classifier has a different weight factor Adaboost Adaboost is short for adaptive boosting.

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Preliminaries Basic machine learning concepts Probability Theory concepts expectation correlated random variable Analysis of Committees1 The committee is a native inspiration for how to combine several models(or we can say how to combine the outputs of several models). For example, we can combine all the models by: [ y_{COM}(X)=frac{1}{M}sum_{m=1}^My_m(X)tag{1} ] Then we want to find out whether this average prediction of models is better than every one of them.

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Preliminaries Bayesian Theorem Bayesian Model Averaging(BMA)1 Bayesian model averaging(BMA) is another wildly used method that is very like a combining model. However, the difference between BMA and combining models is also significant. A Bayesian model averaging is a Bayesian formula in which the random variable are models(hypothesizes) (h=1,2,cdots,H) with prior probability (Pr(h)), then the marginal distribution over data (X) is: [ Pr(X)=sum_{h=1}^{H}Pr(X|h)Pr(h) ] And the MBA is used to select a model(hypothesis) that can model the data best […]

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Preliminaries ‘Mixtures of Gaussians’ Basic machine learning concepts Combining Models1 The mixture of Gaussians had been discussed in the post ‘Mixtures of Gaussians’. It was used to introduce the ‘EM algorithm’ but it gave us the inspiration of improving model performance. All models we have studied, besides neural networks, are all single-distribution models. That is just like that, to solve a problem we invite an expert who is very good at this kind of problem, then we just […]

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When working on Spark jobs (in Scala), we often sequentially write the code in a single class, giving more attention to the transformations we do and forgetting how our code is structured or even if it’s tested. Today I’ll be talking about how I personally like to structure and design my Spark jobs, such as they are highly maintainable and testable.

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