Since I ve slowed down with interesting blogging, I thought I d do some lazy self-promotion and share the slides for three recent talks. The first (hosted by the Finnish Center for Artificial Intelligence) was on Monte Carlo Variational Inference. This talk tries to summarize recent work on The second talk (for the Measure Transport, Diffusion Processes Continue reading Three recent talks
Say you get a large group of people and split them into three groups. The first group gets this question: What is the length of the Mississippi river? The second group gets these questions: Is the Mississippi river greater or less than 70 miles long? What is the length of the Mississippi river? The third Continue reading Is anchoring a reliable cognitive bias?
You drew 40 random cells from a sample and found that a new drug affected 16 of them. An online calculator told you: With 90% confidence, the true fraction is between 26.9% and 54.2%. But what does this really mean? We ve talked about confidence sets before—read that post first if you find this one too Continue reading More confidence games
Say you re developing a new anti-cancer drug. You apply it to some cell line, draw 40 random cells, and manually inspect them. You find that the drug changed 16 of the 40, suggesting the drug is around 40% effective. But of course, this is just an estimate. So you plug the numbers into an online Continue reading Confidence games
The primacy of warmth Deviation-rejection The Romeo and Juliet effect Single-exposure musical conditioning Stereotype priming Stereotype susceptibility Sex differences in distress from infidelity Psychological distance and moral judgment Cleanliness and moral judgments Physical warmth and interpersonal warmth Moral licensing Superstition and performance Moral behavior and brightness
What I did 1. I defined four simple one-dimensional regression problems using common datasets. For each of those problems, I split the data into a training set and a test set. Here s what that looks like for the boston dataset 2. I took the training points, and plotted them to a .pdf file as black Continue reading The human regression ensemble
Say you ve got a positive dataset and you want to calculate the variance. However, the numbers in your dataset are huge, so huge you need to represent them in the log-domain. How do you compute the log-variance without things blowing up? I ran into the problem today. To my surprise, I couldn t find a standard Continue reading Calculating variance in the log domain
(All based on these excellent slides from Umberto Picchini) Approximate Bayesian Computation sounds like a broad class of methods that would potentially include things like message passing, variational methods, MCMC, etc. However, for historical reasons, the term is used for a very specialized class of methods. The core idea is as follows: Sample from the Continue reading Back to Basics:…
1. Think of theorem statements like an API. Some people feel intimidated by the prospect of putting a theorem into their papers. They feel that their results aren t deep enough to justify this. Instead, they give the derivation and result inline as part of the normal text. Sometimes that s best. However, the purpose of a Continue reading Three opinions on theorems
As part of the graphical models course I taught last spring, I developed a cheatsheet for exponential families. The basic purpose is to explain the standard moment-matching condition of maximum likelihood. The goal of the sheet was to clearly show how this property generalized to maximum likelihood in conditional exponential families, with hidden variables, or Continue reading Exponential Families…