Tag: ML

AI Machine Learning & Data Science Research

Improving ML Fairness: IBM, UMich & ShanghaiTech Papers Focus on Statistical Inference and Gradient-Boosting

A team from University of Michigan, MIT-IBM Watson AI Lab and ShanghaiTech University publishes two papers on individual fairness for ML models, introducing a scale-free and interpretable statistically principled approach for assessing individual fairness and a method for enforcing individual fairness in gradient boosting suitable for non-smooth ML models.

AI Machine Learning & Data Science Research

Microsoft & Princeton’s Surprising Discovery: Text-Game Agents Achieve High Scores in Complete Absence of Semantics

A research team from Princeton University and Microsoft Research discover autonomous language-understanding agents are capable of achieving high scores even in the complete absence of language semantics, indicating that current RL agents for text-based games might not be sufficiently leveraging the semantic structure of game texts.