186 citations · 412 across the 34 of their papers we have counts for
7 papers · 1 filter
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sijia Liu, Jiahao Liu, Hansu Gu +4
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of ite…
Certified Robustness for Large Language Models with Self-Denoising
Zhen Zhang, Guanhua Zhang, Bairu Hou +5
Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses,…
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Jiaheng Wei, Zhaowei Zhu, Gang Niu +4
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning. Most prior works treat either problem in an i…
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?
Yuguang Yao, Jiancheng Liu, Yifan Gong +4
Numerous adversarial attack methods have been developed to generate imperceptible image perturbations that can cause erroneous predictions of state-of-the-art machine learning (ML)…
Text-Visual Prompting for Efficient 2D Temporal Video Grounding
Yimeng Zhang, Xin Chen, Jinghan Jia +2
In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long u…
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Shuai Zhang, Meng Wang, Pin-Yu Chen +3
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and sto…
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning
Soumyadeep Pal, Ren Wang, Yuguang Yao +1
Recent studies on backdoor attacks in model training have shown that polluting a small portion of training data is sufficient to produce incorrect manipulated predictions on poison…