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cs.LG2026
Training ML Models with Predictable Failures
Will Schwarzer, Scott Niekum
Estimating how often an ML model will fail at deployment scale is central to pre-deployment safety assessment, but a feasible evaluation set is rarely large enough to observe the f…
cs.LG2025
Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints
Yaswanth Chittepu, Blossom Metevier, Will Schwarzer +3
Existing approaches to language model alignment often treat safety as a tradeoff against helpfulness, which can lead to unacceptable responses in sensitive domains. To ensure relia…
cs.LG2025
Supervised Reward Inference
Will Schwarzer, Jordan Schneider, Philip S. Thomas +1
Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models. However, humans often indicate their goals throug…