6 papers
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
Dongze Wu, David I. Inouye, Yao Xie
Predicting potential and counterfactual outcomes from observational data is central to individualized decision-making, particularly in clinical settings where treatment choices mus…
Att-Adapter: A Robust and Precise Domain-Specific Multi-Attributes T2I Diffusion Adapter via Conditional Variational Autoencoder
Wonwoong Cho, Yan-Ying Chen, Matthew Klenk +2
Text-to-Image (T2I) Diffusion Models have achieved remarkable performance in generating high quality images. However, enabling precise control of continuous attributes, especially…
Imagine for Me: Creative Conceptual Blending of Real Images and Text via Blended Attention
Wonwoong Cho, Yanxia Zhang, Yan-Ying Chen +1
Blending visual and textual concepts into a new visual concept is a unique and powerful trait of human beings that can fuel creativity. However, in practice, cross-modal conceptual…
Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods
Wonwoong Cho, Hareesh Ravi, Midhun Harikumar +5
As Diffusion Models have shown promising performance, a lot of efforts have been made to improve the controllability of Diffusion Models. However, how to train Diffusion Models to…
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization
Ziyu Gong, Jim Lim, David I. Inouye
Distribution matching (DM) is a versatile domain-invariant representation learning technique that has been applied to tasks such as fair classification, domain adaptation, and doma…
Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments
Surojit Ganguli, Zeyu Zhou, Christopher G. Brinton +1
When each edge device of a network only perceives a local part of the environment, collaborative inference across multiple devices is often needed to predict global properties of t…