5 citations · 11 across the 14 of their papers we have counts for
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Cauchy-Schwarz Fairness Regularizer
Yezi Liu, Hanning Chen, Wenjun Huang +2
Group fairness in machine learning is often enforced by adding a regularizer that reduces the dependence between model predictions and sensitive attributes. However, existing regul…
Mitigating Bias in Graph Hyperdimensional Computing
Yezi Liu, William Youngwoo Chung, Yang Ni +2
Graph hyperdimensional computing (HDC) has emerged as a promising paradigm for cognitive tasks, emulating brain-like computation with high-dimensional vectors known as hypervectors…
LUNE: Efficient LLM Unlearning via LoRA Fine-Tuning with Negative Examples
Yezi Liu, Hanning Chen, Wenjun Huang +2
Large language models (LLMs) possess vast knowledge acquired from extensive training corpora, but they often cannot remove specific pieces of information when needed, which makes i…
Recover-to-Forget: Gradient Reconstruction from LoRA for Efficient LLM Unlearning
Yezi Liu, Hanning Chen, Wenjun Huang +2
Unlearning in large foundation models (e.g., LLMs) is essential for enabling dynamic knowledge updates, enforcing data deletion rights, and correcting model behavior. However, exis…
Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing
Yezi Liu, Prathyush Poduval, Wenjun Huang +3
Graph unlearning is a crucial approach for protecting user privacy by erasing the influence of user data on trained graph models. Recent developments in graph unlearning methods ha…