"This framework is pretty powerful, and it allows to reformulate a bunch of different problems in machine learning and optimization as similar games. More in general, with the regret framework we can analyze situations in which the data are not independent and identically distributed from a distribution, yet I would like to guarantee that the algorithm is “learning” something. For example, online learning can be used to analyze
• Click prediction problems;
• Routing on a network;
• Convergence to equilibrium of repeated games.
It can also be used to analyze stochastic algorithms, e.g., Stochastic Gradient Descent, but the adversarial nature of the analysis might give you suboptimal results. For example, it can be used to analyze momentum algorithms, but the adversarial nature of the losses essentially forces you to prove a convergence guarantee that treats the momentum term as a vanishing disturbance that does not help the algorithm in any way."
Looks pretty interesting, and I'll be coming back to it as a paper, but don't head in there looking for advice on how to score that Udemy course at a deep discount! :)
> "This framework is pretty powerful, and it allows to reformulate a bunch of different problems in machine learning and optimization as similar games.
Agreed. I am economist doing DS and the framing used in the paper makes online learning much more intuitive for me.
Definitely some SEO issues even in peer reviewed research databases.
To me, "online learning" has nothing to do with classes on the internet. I still think of those as "MOOCs". I don't think it's a stretch to say that the author wasn't aware of the other usage, as I'm certainly not.