An Addendum to Alchemy
This post is an addendum to our “test of time” talk at NIPS 2017. We’d like to expand on a few points about the talk we gave at NIPS last week. The talk highlighted the... Continue
Reflections on Random Kitchen Sinks
Ed. Note: Ali Rahimi and I won the test of time award at NIPS 2017 for our paper “Random Features for Large-scale Kernel Machines”. This post is the text of the acceptance speech we wrote.... Continue
Nesterov's Punctuated Equilibrium
Ed. Note: this post is co-written with Roy Frostig. Following the remarkable success of AlphaGo, there has been a groundswell of interest in reinforcement learning for games, robotics, parameter tuning, and even computer networking. In... Continue
The Fall of BIG DATA
I’m still in total shock from the decision my country made last Tuesday. We elected a hateful, bigoted, misogynistic, incompetent demagogue to lead us into a dark and foreboding future. While the internet has been... Continue
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... Continue
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... Continue
The Role of Convergence Analysis
This year marks the retirement of Dimitri Bertsekas from MIT. Dimitri is an idol of mine, having literally written the book on every facet of optimization. His seminal works on distributed optimization, dynamic programming, and... Continue
Mechanics of Lagrangians
In my last post, I used a Lagrangian to compute derivatives of constrained optimization problems in neural nets and control. I took it for granted that the procedure was correct. But why is it correct?... Continue