1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers
Joshua Nathaniel Williams, J. Zico Kolter
The widespread use of large language models has resulted in a multitude of tokenizers and embedding spaces, making knowledge transfer in prompt discovery tasks difficult. In this w…
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…
DrawL: Understanding the Effects of Non-Mainstream Dialects in Prompted Image Generation
Joshua N. Williams, Molly FitzMorris, Osman Aka +1
Text-to-image models are now easy to use and ubiquitous. However, prior work has found that they are prone to recapitulating harmful Western stereotypes. For example, requesting th…
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…