Efficiency Robustness

Efficiency robustness research focuses on ensuring that machine learning models maintain their performance and resource efficiency even under adversarial conditions or data shifts. Current work investigates this across various model types, including large language models and image caption generators, exploring techniques like adversarial training and gradient-based algorithms to improve robustness while minimizing computational costs. This research is crucial for deploying reliable and efficient AI systems in real-world applications, where resource constraints and potential attacks are significant concerns, and for developing verifiable guarantees on model performance.