Aaron Roth

@Aaroth

CS prof at Penn. Amazon Scholar at AWS. Author of The Ethical Algorithm (w/ Michael Kearns). I study machine learning, privacy, game theory, and uncertainty.

Philadelphia, PA

Joined May 2007

Pinned
Aaron Roth
@Aaroth
Jul 25
It was fun giving this tutorial. If you missed it, check out calibration-tutorial.github.io where all of our materials are available, including slides, hundreds of pages of lecture notes, an interactive demo, and an annotated reading list.
Aaron Roth
@Aaroth
Jul 3
On Monday @natalie_collina, Ira, and I are giving a tutorial at ICML on (multi)calibration and its applications. You can find slides and an annotated bibliography on the website: calibration-tutorial.github.io as well as an interactive demo of online calibration algs. See you there!
Aaron Roth
@Aaroth
Aug 14
We have a new online boosting algorithm which is very efficient and effective. Unlike prior algorithms which maintain many weak learners and ensemble them, we operationalize the "dual view" of boosting. We don't maintain an ensemble. We try to construct an online hard core set.
Aaron Roth
@Aaroth
Aug 7
People used to be able to impress and intimidate reviewers with complicated proofs. This will change. In the age of AI inscrutable proofs are cheap. It is understandable proofs that are valuable. Opaque complexity is now it is a sign of laziness or lack of insight.
Aaron Roth
@Aaroth
Aug 1
We are clearly moving to a world that will be without open problems, but this doesn't mean math is going away. Interesting work will correspond to discovering new questions and coherent theoretical frameworks. Well defined agreed-to-be-interesting problems won't last long.
Aaron Roth
@Aaroth
Aug 1
This is an interesting concern. Right now we have "problem overhang" - lots of problems we as a community are interested in because smart and charismatic people thought about them and convinced us that these problems are important. So we are happy/interested to see them solved.
Henry Yuen
@henryquantum
Aug 1
Replying to @henryquantum
6. But -- what then? What happens when AI has answered the handful of my favorite problems that I've spent the last 15 years thinking about? There are a lot of nice problems that I like, but it's not so easy to find a favorite problem.