98 citations · 98 across the 12 of their papers we have counts for
7 papers · 1 filter
A Unified Theory of Random Projection for Influence Functions
Pingbang Hu, Yuzheng Hu, Jiaqi W. Ma +1
Influence functions and related data attribution scores take the form of , where is a curvature operator. In modern overparametrized models,…
Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
Shiji Zhou, Tianbai Yu, Zhi Zhang +4
Machine unlearning (MU) aims to efficiently remove sensitive or harmful memory from a pre-trained model. The key challenge is to balance the potential tradeoff between unlearning e…
Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization
Yuen Chen, Haozhe Si, Guojun Zhang +1
Domain generalization (DG) seeks to develop models that generalize well to unseen target domains, addressing the prevalent issue of distribution shifts in real-world applications.…
Structural Alignment Improves Graph Test-Time Adaptation
Hans Hao-Hsun Hsu, Shikun Liu, Han Zhao +1
Graph-based learning excels at capturing interaction patterns in diverse domains like recommendation, fraud detection, and particle physics. However, its performance often degrades…
Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization
Samuel Schapiro, Han Zhao
Recently, sharpness-aware minimization (SAM) has emerged as a promising method to improve generalization by minimizing sharpness, which is known to correlate well with generalizati…
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
Evelyn Ma, Chao Pan, Rasoul Etesami +2
The performance of Transfer Learning (TL) heavily relies on effective pretraining, which demands large datasets and substantial computational resources. As a result, executing TL i…
Pairwise Alignment Improves Graph Domain Adaptation
Shikun Liu, Deyu Zou, Han Zhao +1
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model…