Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
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…
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…