168 citations · 252 across the 25 of their papers we have counts for
27 papers
Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models
Jack He, Jianxing Zhao, Andrew Bai +1
In the rapidly evolving landscape of artificial intelligence, generative models such as Generative Adversarial Networks (GANs) and Diffusion Models have become cornerstone technolo…
Mitigating Bias in Dataset Distillation
Justin Cui, Ruochen Wang, Yuanhao Xiong +1
Dataset Distillation has emerged as a technique for compressing large datasets into smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study…
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
Ruochen Wang, Sohyun An, Minhao Cheng +3
Large Language Models (LLMs) exhibit strong generalization capabilities to novel tasks when prompted with language instructions and in-context demos. Since this ability sensitively…
On Discrete Prompt Optimization for Diffusion Models
Ruochen Wang, Ting Liu, Cho-Jui Hsieh +1
This paper introduces the first gradient-based framework for prompt optimization in text-to-image diffusion models. We formulate prompt engineering as a discrete optimization probl…
Large Language Models are Interpretable Learners
Ruochen Wang, Si Si, Felix Yu +3
The trade-off between expressiveness and interpretability remains a core challenge when building human-centric predictive models for classification and decision-making. While symbo…
MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?
Xirui Li, Hengguang Zhou, Ruochen Wang +3
Humans are prone to cognitive distortions -- biased thinking patterns that lead to exaggerated responses to specific stimuli, albeit in very different contexts. This paper demonstr…