101 citations · 193 across the 17 of their papers we have counts for
5 papers · 1 filter
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution
Shuang Ao, Tianyi Zhou, Guodong Long +2
Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes. In contrast, current reinforcement learn…
Empirical Optimal Risk to Quantify Model Trustworthiness for Failure Detection
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Failure detection (FD) in AI systems is a crucial safeguard for the deployment for safety-critical tasks. The common evaluation method of FD performance is the Risk-coverage (RC) c…
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing
Shuang Ao
Although AI systems have been applied in various fields and achieved impressive performance, their safety and reliability are still a big concern. This is especially important for…
Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Proper confidence calibration of deep neural networks is essential for reliable predictions in safety-critical tasks. Miscalibration can lead to model over-confidence and/or under-…
Confidence-Aware Calibration and Scoring Functions for Curriculum Learning
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Despite the great success of state-of-the-art deep neural networks, several studies have reported models to be over-confident in predictions, indicating miscalibration. Label Smoot…