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#92 - SARA HOOKER - Fairness, Interpretability, Language Models

Machine Learning Street Talk (MLST)

CHAPTER

Enhancing Machine Learning through Ensemble Methods and Data Strategies

This chapter focuses on the efficacy of ensemble methods for improving model performance, especially in addressing low-frequency errors. It discusses the importance of distinguishing data uncertainties and optimizing methodologies for better interpretability. Additionally, it explores topics like reinforcement learning from human feedback and implications on aligning models with human values.

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