The title of this blog post is a paper that recently appeared at KDD. Here is a link that probably won't work. This post will be a running commentary on the paper as I read it.

A major bottleneck in some of our analyses is that we need to resample survey responses. Let \(n\) denote the number of responses we’ve seen. Let \(m\) denote the number of questions in the survey. \(b_i\) is the number of bootstrap iterations. \(b_s\) is a list of bootstrap samples. \(scores\) is a list of scores. Our resampling approach is as follows:

A few months ago, Emery pointed out a new project being advertised on Stanford's CS web page. It's called 3x and describes the system:

3X is an open-source software tool to ease the burden of conducting computational experiments and managing data analytics. 3X provides a standard yet configurable structure to execute a wide variety of experiments in a systematic way, avoiding repeated creation of ad-hoc scripts and directory hierarchies. 3X organizes the code, inputs, and outputs for an experiment. The tool submits arbitrary numbers of computational runs to a variety of different compute platforms, and supervises their execution. It records the returning results, and lets the experimenter immediately visualize the data in a variety of ways. Aggregated result data shown by the tool can be drilled down to individual runs, and further runs of the experiment can be driven interactively. Our ultimate goal is to make 3X a “smart assistant” that runs experiments and analyzes results semi-automatically, so experimenters and analysts can focus their time on deeper analysis. Two features toward this end are under development: visualization recommendations and automatic selection of promising runs.

About a month ago, Charles Sutton stopped by UMass to give a talk called "Statistical Analysis Of Computer Programs." Here are my lightly-edited, cleaned-up (ish) notes on the talk (this approach inspired by ezyang's amazing note-taking abilities):

Some time ago Cibele and I were working on a fun logic project that involved encoding classical logic in Coq. Perhaps it seems odd that we would write a logic language in language that has a logic already encoded in it, but this is a classic application of PL goodness (i.e., nonsense): we used Gallina as our meta language to encode the primitives of our target language (propositional logic, for starters). You can see the fruits of our efforts here.