6 papers
Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
Jian-Feng Cai, Haixia Liu, Zhengyi Su +1
Classifier-free guidance (CFG) is a widely used technique for controllable generation in diffusion and flow-based models. Despite its empirical success, CFG relies on a heuristic l…
MAP-based Problem-Agnostic diffusion model for Inverse Problems
Pingping Tao, Haixia Liu, Jing Su
Diffusion models have indeed shown great promise in solving inverse problems in image processing. In this paper, we propose a novel, problem-agnostic diffusion model called the max…
BlockRR: A Unified Framework of RR-type Algorithms for Label Differential Privacy
Haixia Liu, Yi Ding
In this paper, we introduce BlockRR, a novel and unified randomized-response mechanism for label differential privacy. This framework generalizes existed RR-type mechanisms as spec…
RPWithPrior: Label Differential Privacy in Regression
Haixia Liu, Ruifan Huang
With the wide application of machine learning techniques in practice, privacy preservation has gained increasing attention. Protecting user privacy with minimal accuracy loss is a…
Fairness via Independence: A (Conditional) Distance Covariance Framework
Ruifan Huang, Haixia Liu
We explore fairness from a statistical perspective by selectively utilizing either conditional distance covariance or distance covariance statistics as measures to assess the indep…
The Exploration of Neural Collapse under Imbalanced Data
Haixia Liu
Neural collapse, a newly identified characteristic, describes a property of solutions during model training. In this paper, we explore neural collapse in the context of imbalanced…