Performance Guarantee

Performance guarantees in machine learning and optimization aim to provide mathematically rigorous bounds on the accuracy, reliability, or efficiency of algorithms and models. Current research focuses on developing such guarantees for diverse applications, including reinforcement learning (using methods like policy gradients and belief-space planning), optimization algorithms (both classical and learned), and various machine learning models (e.g., large language models, recurrent neural networks, and Gaussian processes). Establishing these guarantees is crucial for deploying these technologies in safety-critical applications and for building trust in their predictions and decisions.