1 citations · 1 across the 10 of their papers we have counts for
12 papers
When Trees Are Not Enough: Learning Mixed-Topology Feature Graphs with Adaptive Graph Sparse Autoencoders
Xiaozuo Shen, Yifei Cai, Tian Tan +3
Sparse autoencoders (SAEs) expose interpretable features in large language model activations, yet existing structured SAEs impose single-parent trees or forests, while post-hoc gra…
CST: Collaborative Selective Transmission for Communication-Efficient Multimodal Edge Inference
Hai Chi, Junrui Zhang, Rui Ning +4
Collaborative multimodal inference improves edge perception by combining observations from distributed sensing devices, but transmitting high-dimensional helper representations inc…
Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks
Junrui Zhang, Zemin Chen, Lusi Li +3
Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studi…
HarmQ: Harmonic Backdoor Attacks Against Quantum Neural Networks
Junrui Zhang, Zemin Chen, Chunsheng Xin +2
Quantum Neural Networks (QNNs) have emerged as a promising paradigm for quantum machine learning in the Noisy Intermediate-Scale Quantum (NISQ) era, leveraging quantum phenomena su…
Laplace-Bridged Randomized Smoothing for Fast Certified Robustness
Miao Lin, MD Saifur Rahman Mazumder, Feng Yu +2
Randomized Smoothing (RS) offers formal guarantees for arbitrary base classifiers but faces two key practical bottlenecks: (i) it often relies on noise-augmented training…
DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers
Xiaozuo Shen, Yifei Cai, Rui Ning +2
The widespread adoption of Vision Transformers (ViTs) elevates supply-chain risk on third-party model hubs, where an adversary can implant backdoors into released checkpoints. Exis…
RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance
Miao Lin, Feng Yu, Rui Ning +6
Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world sce…
Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment
Yiming Du, Ziyu Wang, Jian Li +2
Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing mi…
PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
Sindhuja Madabushi, Haider Ali, Ahmad Faraz Khan +5
Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential,…
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
Jiaqi Xue, Mayank Kumar, Yuzhang Shang +5
Federated Learning (FL) enables collaborative model training across institutions without sharing raw data. However, gradient sharing still risks privacy leakage, such as gradient i…
Heterogeneous Graph Backdoor Attack
Jiawei Chen, Lusi Li, Daniel Takabi +2
Heterogeneous Graph Neural Networks (HGNNs) excel in modeling complex, multi-typed relationships across diverse domains, yet their vulnerability to backdoor attacks remains unexplo…
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
Xiangrui Xu, Qiao Zhang, Rui Ning +2
The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essent…