High Probability Guarantee

High probability guarantees in machine learning aim to provide rigorous mathematical assurances about the performance and reliability of algorithms, moving beyond average-case analysis. Current research focuses on developing such guarantees for diverse areas, including reinforcement learning (e.g., in partially observable environments), differentially private stochastic gradient descent (addressing heavy-tailed gradient distributions), and fairness-aware model training. These advancements are crucial for building trustworthy and robust AI systems, enabling reliable deployment in high-stakes applications and fostering greater confidence in the field.