Rigor in Machine Learning
Sherri emphasizes the importance of rigor and transparency in machine learning research, particularly when it comes to estimators and causal inference. She warns against the pitfalls of cherry-picking results and stresses the need for clear communication about the goals and limitations of analyses. Establishing high standards and sharing code are essential for fostering a culture of genuine discovery in the field.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374
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