I have a dirty secret. Well, I actually have many. But one of them is that I never understood the basic algorithms behind reinforcement learning. So I plan to remedy this with a series of blog posts, where I will cover some of the basic RL algorithms, from REINFORCE to …

Most proofs in optimization consist in using inequalities for a particular function class in some creative way. This is a cheatsheet with inequalities that I use most often. It considers class of functions that are convex, strongly convex and $L$-smooth.

My friend Rémi Leblond has recently uploaded to ArXiv our preprint on an asynchronous version of the SAGA optimization algorithm.

The main contribution is to develop a parallel (fully asynchronous, no locks) variant of the SAGA algorighm. This is a stochastic variance-reduced method for general optimization, specially adapted for problems …