9 citations · 25 across the 22 of their papers we have counts for
27 papers
RA: Retain-Anchored Attraction Preserves Forget-Adjacent Utility in LLM Unlearning
Zinan Ling, Yang Xiao, Ruimeng Ye +2
Large Language Model (LLM) unlearning is often evaluated primarily by target suppression and clean retain utility. These metrics can miss a local failure mode: a model may perform…
Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning
Yang Xiao, Kaiyuan Deng, Gen Li +4
Diffusion models pose significant risks regarding the generation of illicit content and copyright violations. This has catalyzed the development of machine unlearning to erase harm…
Your Unlearning Gives You Away: Identifying Erased Concepts in Diffusion Models
Kaiyuan Deng, Yuchen Li, Yang Xiao +3
Existing attacks on unlearned diffusion models assume that the erased concepts are known in advance and focus on recovering them. In practice, however, model providers may not disc…
No Concept Escapes the Audit: Auditing-Aware Unlearning for Verifiable Concept Erasure in Diffusion Models
Kaiyuan Deng, Yuchen Li, Gen Li +5
Text-to-image diffusion models can generate prohibited content, which motivates concept erasure through machine unlearning. Most erasure methods intervene at the text interface, th…
On the Fragility of Data Attribution When Learning Is Distributed
Xian Gao, Bo Hui, Min-Te Sun +1
Data attribution has become an important component of pricing, auditing, and governance in machine learning pipelines, yet most attribution methods implicitly assume that attributi…
Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation
Yang Xiao, Huiyuan Chen, Kaiyuan Deng +6
We propose \textbf{Compressed Video Aggregator} (CVA), a lightweight micro-video recommendation module that decouples video information from preference learning. CVA first summariz…
Your Language Model Secretly Contains Personality Subnetworks
Ruimeng Ye, Zihan Wang, Zinan Ling +4
Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Ex…
Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking
Kaiyuan Deng, Bo Hui, Gen Li +4
The widespread adoption of text-to-image (T2I) diffusion models has raised concerns about their potential to generate copyrighted, inappropriate, or sensitive imagery. As a practic…
Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models
Kaiyuan Deng, Gen Li, Yang Xiao +2
Text-to-image diffusion models have achieved remarkable progress, yet their use raises copyright and misuse concerns, prompting research into machine unlearning. However, extending…
From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment of LLMs
Kaiyuan Deng, Hangyu Zheng, Minghai Qing +11
Deploying models, especially large language models (LLMs), is becoming increasingly attractive to a broader user base, including those without specialized expertise. However, due t…
LightCache: Memory-Efficient, Training-Free Acceleration for Video Generation
Yang Xiao, Gen Li, Kaiyuan Deng +5
Training-free acceleration has emerged as an advanced research area in video generation based on diffusion models. The redundancy of latents in diffusion model inference provides a…
Efficient Knowledge Graph Unlearning with Zeroth-order Information
Yang Xiao, Ruimeng Ye, Bohan Liu +2
Due to regulations like the Right to be Forgotten, there is growing demand for removing training data and its influence from models. Since full retraining is costly, various machin…
Weak-to-Strong Generalization with Failure Trajectories: A Tree-based Approach to Elicit Optimal Policy in Strong Models
Ruimeng Ye, Zihan Wang, Yang Xiao +3
Weak-to-Strong generalization (W2SG) is a new trend to elicit the full capabilities of a strong model with supervision from a weak model. While existing W2SG studies focus on simpl…
The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models
Yang Xiao, Gen Li, Jie Ji +3
Machine unlearning aims to efficiently eliminate the memory about deleted data from trained models and address the right to be forgotten. Despite the success of existing unlearning…
Demystify Protein Generation with Hierarchical Conditional Diffusion Models
Zinan Ling, Yi Shi, Brett McKinney +3
Generating novel and functional protein sequences is critical to a wide range of applications in biology. Recent advancements in conditional diffusion models have shown impressive…
Optimized Local Updates in Federated Learning via Reinforcement Learning
Ali Murad, Bo Hui, Wei-Shinn Ku
Federated Learning (FL) is a distributed framework for collaborative model training over large-scale distributed data, enabling higher performance while maintaining client data pri…
Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization
Gen Li, Yang Xiao, Jie Ji +4
Text-to-image (T2I) diffusion models have achieved remarkable success in generating high-quality images from textual prompts. However, their ability to store vast amounts of knowle…
Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing
Yang Xiao, Wang Lu, Jie Ji +4
The design of artificial neural networks (ANNs) is inspired by the structure of the human brain, and in turn, ANNs offer a potential means to interpret and understand brain signals…
DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning
Bohan Liu, Yang Xiao, Ruimeng Ye +3
Distributed backdoor attacks (DBA) have shown a higher attack success rate than centralized attacks in centralized federated learning (FL). However, it has not been investigated in…
Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective
Yuchen Fang, Yuxuan Liang, Bo Hui +5
Road traffic forecasting is crucial in real-world intelligent transportation scenarios like traffic dispatching and path planning in city management and personal traveling. Spatio-…
Weak-to-Strong Generalization beyond Accuracy: a Pilot Study in Safety, Toxicity, and Legal Reasoning
Ruimeng Ye, Yang Xiao, Bo Hui
As large language models (LLMs) continue to advance, ensuring their alignment with human values becomes increasingly critical. Traditional alignment methods heavily rely on human f…
A Survey of Lottery Ticket Hypothesis
Bohan Liu, Zijie Zhang, Peixiong He +6
The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i.e., winning tickets) that can achieve even better performance th…
Successfully Applying Lottery Ticket Hypothesis to Diffusion Model
Chao Jiang, Bo Hui, Bohan Liu +1
Despite the success of diffusion models, the training and inference of diffusion models are notoriously expensive due to the long chain of the reverse process. In parallel, the Lot…
Rethinking Graph Lottery Tickets: Graph Sparsity Matters
Bo Hui, Da Yan, Xiaolong Ma +1
Lottery Ticket Hypothesis (LTH) claims the existence of a winning ticket (i.e., a properly pruned sub-network together with original weight initialization) that can achieve competi…