98 citations · 98 across the 12 of their papers we have counts for
12 papers
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,…
FM SO.P: A Progressive Task Mixture Framework with Automatic Evaluation for Cross-Domain SOP Understanding
Siyuan Huang, Ziyu Wang, Chao Pan +1
Standard Operating Procedures (SOPs) are critical for enterprise operations, yet existing language models struggle with SOP understanding and cross-domain generalization. Current m…
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.…
MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning
Jingyan Shen, Jiarui Yao, Rui Yang +5
Reward modeling is a key step in building safe foundation models when applying reinforcement learning from human feedback (RLHF) to align Large Language Models (LLMs). However, rew…
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…
On the Expressive Power of Tree-Structured Probabilistic Circuits
Lang Yin, Han Zhao
Probabilistic circuits (PCs) have emerged as a powerful framework to compactly represent probability distributions for efficient and exact probabilistic inference. It has been show…
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…
CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent Layers
Longwei Zou, Qingyang Wang, Han Zhao +3
The fast-growing large scale language models are delivering unprecedented performance on almost all natural language processing tasks. However, the effectiveness of large language…
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…
A survey of recent methods for addressing AI fairness and bias in biomedicine
Yifan Yang, Mingquan Lin, Han Zhao +3
Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reduci…