The News on Auto-tuning
Ed. Note: this post is in my voice, but it was co-written with Kevin Jamieson. Kevin provided the awesome plots too. It’s all the rage in machine learning these days to build complex, deep pipelines with thousands of tunable parameters. Now, I don’t mean parameters that we learn by stochastic gradient descent. But I mean architectural concerns, like the value of the regularization parameter, the s
Embracing the Random
Ed. Note: this post is again in my voice, but co-written with Kevin Jamieson. Kevin provided all of the awesome plots, and has a great tutorial for implementing the algorithm I’ll describe in this post In the last post, I argued that random search is a competitive method for black-box parameter tuning in machine learning. This is actually great news! Random search is a incredibly simple algorithm,
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