1 citations · 2 across the 13 of their papers we have counts for
15 papers
Demystifying the Optimal Fair Classifier in Multi-Class Classification
Li Zhang, Yuyuan Li, XiaoHua Feng +3
Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent i…
Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systems
Jiaming Zhang, Yuyuan Li, Xiaohua Feng +4
Large Language Model-based Recommender Systems (LRSs) have recently emerged as a new paradigm in sequential recommendation by directly adopting LLMs as backbones. While LRSs demons…
UFO: Unfair-to-Fair Evolving Mitigates Unfairness in LLM-based Recommender Systems via Self-Play Fine-tuning
Jiaming Zhang, Yuyuan Li, Xiaohua Feng +3
Large language model-based Recommender Systems (LRSs) have demonstrated superior recommendation performance by integrating pre-training with Supervised Fine-Tuning (SFT). However,…
TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models
Li Zhang, Zhongxuan Han, XiaoHua Feng +5
Efficient and lightweight adaptation of pre-trained Vision-Language Models (VLMs) to downstream tasks through collaborative interactions between local clients and a central server…
BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
Jiaming Zhang, Yuyuan Li, Yiqun Xu +4
Large Language Model-enhanced Recommender Systems (LLM-enhanced RSs) have emerged as a powerful approach to improving recommendation quality by leveraging LLMs to generate item rep…
Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols
Xiaohua Feng, Jiaming Zhang, Fengyuan Yu +7
With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate these risks, machine unlearning has…
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
Li Zhang, Zhongxuan Han, Xiaohua Feng +3
With the emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e…
RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systems
Xiaohua Feng, Yuyuan Li, Fengyuan Yu +5
In various networks and mobile applications, users are highly susceptible to attribute inference attacks, with particularly prevalent occurrences in recommender systems. Attackers…
A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty
Xiaohua Feng, Yuyuan Li, Chengye Wang +3
Driven by privacy protection laws and regulations, unlearning in Large Language Models (LLMs) is gaining increasing attention. However, current research often neglects the interpre…
Leveraging Machine Unlearning for Cost-Efficient Preference Alignment
Xiaohua Feng, Yuyuan Li, Huwei Ji +4
Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream methods like reinforcement learning with human feedback face notable challenges. These ap…
LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning
Li Zhang, Chaochao Chen, Zhongxuan Han +2
Federated learning (FL) has garnered considerable interest for its capability to learn from decentralized data sources. Given the increasing application of FL in decision-making sc…
WassFFed: Wasserstein Fair Federated Learning
Zhongxuan Han, Li Zhang, Chaochao Chen +4
Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training da…
CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
Chaochao Chen, Jiaming Zhang, Yizhao Zhang +5
With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models.…
Controllable Unlearning for Image-to-Image Generative Models via -Constrained Optimization
Xiaohua Feng, Yuyuan Li, Chaochao Chen +4
While generative models have made significant advancements in recent years, they also raise concerns such as privacy breaches and biases. Machine unlearning has emerged as a viable…
Tycoon: an Implementation of a Distributed, Market-based Resource Allocation System
Kevin Lai, Lars Rasmusson, Eytan Adar +3
Distributed clusters like the Grid and PlanetLab enable the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. One major challeng…