3 papers
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.SD2026
Are Deep Speech Denoising Models Robust to Adversarial Noise?
Will Schwarzer, Neel Chaudhari, Philip S. Thomas +2
Deep noise suppression (DNS) models enjoy widespread use throughout a variety of high-stakes speech applications. However, we show that four recent DNS models can each be reduced t…
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…