1 citations · 1 across the 5 of their papers we have counts for
5 papers
Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
Austin T. Hoag, Apostolos Modas, Yunhao Ba +9
Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property (IP). In this technical report, we prese…
Yes, But Not Always. Generative AI Needs Nuanced Opt-in
Wiebke Hutiri, Morgan Scheuerman, Shruti Nagpal +2
This paper argues that a one-size-fits-all approach to specifying consent for the use of creative works in generative AI is insufficient. Real-world ownership and rights holder str…
Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inference
Yuhong Luo, Austin Hoag, Xintong Wang +2
Representation learning is increasingly applied to generate representations that generalize well across multiple downstream tasks. Ensuring fairness guarantees in representation le…
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…
Learning Fair Representations with High-Confidence Guarantees
Yuhong Luo, Austin Hoag, Philip S. Thomas
Representation learning is increasingly employed to generate representations that are predictive across multiple downstream tasks. The development of representation learning algori…