Reporting Statistical Uncertainty
Rishabh explores the importance of reporting statistical uncertainties in algorithm performance, emphasizing that results can vary significantly based on random seeds. By aggregating data across multiple tasks and runs, he illustrates how bootstrapping can provide a more reliable measure of variation, ultimately enhancing the evaluation of random variables in machine learning experiments.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559
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