I went on Uri Goren, Tamir Nave, and Michael Erlihson's podcast "explAInable" (in Hebrew) to talk about uncertainty quantification literature in AI, nuances about what their algorithms do and what they don't, and in particular about my 2021 LSF paper with Yuyang (Bernie) Wang, Tim Januschowski and Jan Gasthaus for making point prediction algorithms (like XGBoost) into probabilistic ones. If you are interested but do not know Hebrew, see https://lnkd.in/g9SGPwbd for a plain English explanation of the algorithm, and for hassle-free copy-and-paste code so you can use it yourself within seconds. (And a link to the paper, of course.)
פרק חדש של explainable עכשיו באוויר 🎙️ והפעם צלילה עמוקה לחיזוי קונפורמי עם Hilaf Hasson ספרו לנו מה חשבתם! Michael Erlihson Tamir Nave https://lnkd.in/dDUZ7cXE