3 papers
cs.CV2025
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
Yutong He, Alexander Robey, Naoki Murata +7
Prompt engineering is an effective but labor-intensive way to control text-to-image (T2I) generative models. Its time-intensive nature and complexity have spurred the development o…
cs.CV2025
Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers
Joshua Nathaniel Williams, Avi Schwarzschild, Yutong He +1
Recovering natural language prompts for image generation models, solely based on the generated images is a difficult discrete optimization problem. In this work, we present the fir…
cs.LG2024
Rethinking Distance Metrics for Counterfactual Explainability
Joshua Nathaniel Williams, Anurag Katakkar, Hoda Heidari +1
Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by gen…