5 citations · 14 across the 7 of their papers we have counts for
7 papers
Flow Straight and Fast in Hilbert Space: Functional Rectified Flow
Jianxin Zhang, Clayton Scott
Many generative models originally developed in finite-dimensional Euclidean space have functional generalizations in infinite-dimensional settings. However, the extension of rectif…
Semantic Editing with Coupled Stochastic Differential Equations
Jianxin Zhang, Clayton Scott
Editing the content of an image with a pretrained text-to-image model remains challenging. Existing methods often distort fine details or introduce unintended artifacts. We propose…
Label Noise: Ignorance Is Bliss
Yilun Zhu, Jianxin Zhang, Aditya Gangrade +1
We establish a new theoretical framework for learning under multi-class, instance-dependent label noise. This framework casts learning with label noise as a form of domain adaptati…
Label Embedding via Low-Coherence Matrices
Jianxin Zhang, Clayton Scott
Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching m…
Learning from Label Proportions by Learning with Label Noise
Jianxin Zhang, Yutong Wang, Clayton Scott
Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed…
Learning from Label Proportions: A Mutual Contamination Framework
Clayton Scott, Jianxin Zhang
Learning from label proportions (LLP) is a weakly supervised setting for classification in which unlabeled training instances are grouped into bags, and each bag is annotated with…
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions
Clayton Scott, Jianxin Zhang
We study binary classification in the setting where the learner is presented with multiple corrupted training samples, with possibly different sample sizes and degrees of corruptio…