no code implementations • 8 Feb 2025 • Bo Ni, Zheyuan Liu, Leyao Wang, Yongjia Lei, Yuying Zhao, Xueqi Cheng, Qingkai Zeng, Luna Dong, Yinglong Xia, Krishnaram Kenthapadi, Ryan Rossi, Franck Dernoncourt, Md Mehrab Tanjim, Nesreen Ahmed, Xiaorui Liu, Wenqi Fan, Erik Blasch, Yu Wang, Meng Jiang, Tyler Derr
Although various methods have been developed to improve the trustworthiness of RAG methods, there is a lack of a unified perspective and framework for research in this topic.
no code implementations • 2 Feb 2025 • Zhichao Hou, Weizhi Gao, Hamid Krim, Xiaorui Liu
This work investigates a novel approach to boost adversarial robustness and generalization by incorporating structural prior into the design of deep learning models.
no code implementations • 5 Nov 2024 • Wei Wang, Zhichao Hou, Xiaorui Liu, Xinxia Peng
Research on long non-coding RNAs (lncRNAs) has garnered significant attention due to their critical roles in gene regulation and disease mechanisms.
1 code implementation • 1 Nov 2024 • Weizhi Gao, Zhichao Hou, Han Xu, Xiaorui Liu
Existing certified defenses for DEQs employing deterministic certification methods such as interval bound propagation and Lipschitz-bounds can not certify on large-scale datasets.
1 code implementation • 30 Oct 2024 • Zhichao Hou, Weizhi Gao, Yuchen Shen, Feiyi Wang, Xiaorui Liu
Transformer-based architectures have dominated various areas of machine learning in recent years.
no code implementations • 7 Oct 2024 • Rui Xue, Tong Zhao, Neil Shah, Xiaorui Liu
Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning, and various sampling approaches have been proposed to scale GNNs to applications with large-scale graphs.
no code implementations • 6 Oct 2024 • Zhichao Hou, MohamadAli Torkamani, Hamid Krim, Xiaorui Liu
This work tackles an intriguing and fundamental open challenge in representation learning: Given a well-trained deep learning model, can it be reprogrammed to enhance its robustness against adversarial or noisy input perturbations without altering its parameters?
no code implementations • 23 Apr 2024 • Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability.
no code implementations • 25 Mar 2024 • Yunqing Li, Xiaorui Liu, Binil Starly
To address the need, this study proposes a Graph Neural Network-based method for manufacturing service capability identification over a knowledge graph.
1 code implementation • 21 Feb 2024 • Jiahao Zhang, Rui Xue, Wenqi Fan, Xin Xu, Qing Li, Jian Pei, Xiaorui Liu
In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy.
no code implementations • 14 Feb 2024 • Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao, Venkataramana Kini, Devendra Yadav, Fei Wang, Zhen Wen, Jiliang Tang, Hui Liu
This design stems from our empirical observation that beam search decoding is ultimately unnecessary for sequential recommendations.
no code implementations • 7 Dec 2023 • Rui Xue, Xipeng Shen, Ruozhou Yu, Xiaorui Liu
In this study, we introduce LEADING, a novel and efficient approach for end-to-end fine-tuning of language models on TAGs.
1 code implementation • 25 Nov 2023 • Zhichao Hou, Ruiqi Feng, Tyler Derr, Xiaorui Liu
The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of numerous defenses.
no code implementations • 16 Jul 2023 • Wendi Yu, Zhichao Hou, Xiaorui Liu
Polynomial graph filters have been widely used as guiding principles in the design of Graph Neural Networks (GNNs).
no code implementations • 3 Jun 2023 • Zhichao Hou, Xitong Zhang, Wei Wang, Charu C. Aggarwal, Xiaorui Liu
This work presents the first investigation into the robustness of GNNs in the context of directed graphs, aiming to harness the profound trust implications offered by directed graphs to bolster the robustness and resilience of GNNs.
1 code implementation • 3 Feb 2023 • Rui Xue, Haoyu Han, MohamadAli Torkamani, Jian Pei, Xiaorui Liu
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs).
no code implementations • 18 Oct 2022 • Han Xu, Xiaorui Liu, Yuxuan Wan, Jiliang Tang
We demonstrate that the fairly trained classifiers can be greatly vulnerable to such poisoning attacks, with much worse accuracy & fairness trade-off, even when we apply some of the most effective defenses (originally proposed to defend traditional classification tasks).
1 code implementation • 15 Jun 2022 • Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang
In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.
no code implementations • 8 Jun 2022 • Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi, Victor Lee, Jiliang Tang
Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.
no code implementations • 1 Jun 2022 • Yuxuan Wan, Han Xu, Xiaorui Liu, Jie Ren, Wenqi Fan, Jiliang Tang
However, federated learning is still under the risk of privacy leakage because of the existence of attackers who deliberately conduct gradient leakage attacks to reconstruct the client data.
no code implementations • 2 May 2022 • Yaxin Li, Xiaorui Liu, Han Xu, Wentao Wang, Jiliang Tang
Deep Neural Network (DNN) are vulnerable to adversarial attacks.
1 code implementation • NeurIPS 2021 • Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.
no code implementations • 29 Sep 2021 • Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang
In this paper, we observe a new issue in deeper GNNs, i. e., feature overcorrelation, and perform a thorough study to deepen our understanding on this issue.
1 code implementation • 12 Aug 2021 • Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li
The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.
no code implementations • 10 Aug 2021 • Yao Li, Xiaorui Liu, Jiliang Tang, Ming Yan, Kun Yuan
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottleneck in practice.
no code implementations • 7 Aug 2021 • Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, JianPing Wang, Charu Aggarwal
Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.
no code implementations • 28 Jul 2021 • Wentao Wang, Han Xu, Xiaorui Liu, Yaxin Li, Bhavani Thuraisingham, Jiliang Tang
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks.
no code implementations • 12 Jul 2021 • Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, Jiliang Tang
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.
1 code implementation • 5 Jul 2021 • Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang
While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing.
no code implementations • ICLR 2022 • Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang
We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions.
1 code implementation • ICLR 2022 • Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
no code implementations • 9 Jun 2021 • Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang
In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.
no code implementations • 10 May 2021 • Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.
no code implementations • 9 Apr 2021 • Xiaorui Liu, Juan Ospina, Ioannis Zografopoulos, Alonzo Russell, Charalambos Konstantinou
Namely, the provided acceleration can be used for improving system scheduling, assessing system vulnerabilities, and predicting system disruptions in real-time systems.
no code implementations • 25 Mar 2021 • Xiaorui Liu, Yaodan Hu, Charalambos Konstantinou, Yier Jin
Our simulation experiments based on the load data from New York state demonstrate that CHIMERA can effectively mitigate 91. 74% of the cases in which FDIAs can maliciously modify the contingencies.
no code implementations • 25 Jan 2021 • Ioannis Zografopoulos, Juan Ospina, Xiaorui Liu, Charalambos Konstantinou
Leveraging the threat model formulation, we present a CPS framework designed to delineate the hardware, software, and modeling resources required to simulate the CPS and construct high-fidelity models which can be used to evaluate the system's performance under adverse scenarios.
Cryptography and Security Systems and Control Systems and Control
2 code implementations • 13 Oct 2020 • Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, Jiliang Tang
However, we find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data.
1 code implementation • 5 Oct 2020 • Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah
In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption.
no code implementations • 2 Sep 2020 • Han Xu, Ya-Xin Li, Xiaorui Liu, Hui Liu, Jiliang Tang
Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem.
no code implementations • ICLR 2021 • Xiaorui Liu, Yao Li, Rongrong Wang, Jiliang Tang, Ming Yan
Communication compression has become a key strategy to speed up distributed optimization.
3 code implementations • 20 May 2020 • Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang
A natural idea to defend adversarial attacks is to clean the perturbed graph.
no code implementations • 16 Oct 2019 • Xiaorui Liu, Yao Li, Jiliang Tang, Ming Yan
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms.
no code implementations • 27 Feb 2019 • Tyler Derr, Hamid Karimi, Xiaorui Liu, Jiejun Xu, Jiliang Tang
Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure.
no code implementations • 6 Nov 2017 • Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang
Dialogue systems have attracted more and more attention.
no code implementations • 7 Nov 2015 • Xiaorui Liu, Yichao Huang, Xin Zhang, Lianwen Jin
We introduce a new pipeline for hand localization and fingertip detection.