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no code implementations • 8 Dec 2022 • Ergute Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Hong Meng Tan, Khin Mi Mi Aung

Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern.

1 code implementation • 20 Nov 2022 • Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang, Kenji Kawaguchi, Xiaokui Xiao

To answer this question, we theoretically study the concentration property of features obtained by neighborhood aggregation on homophilic and heterophilic graphs, introduce the single-pass graph contrastive learning loss based on the property, and provide performance guarantees for the minimizer of the loss on downstream tasks.

no code implementations • 18 Oct 2022 • Jianxin Wei, Ergute Bao, Xiaokui Xiao, Yin Yang

A classic mechanism for this purpose is DP-SGD, which is a differentially private version of the stochastic gradient descent (SGD) optimizer commonly used for DNN training.

1 code implementation • 15 Oct 2022 • Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao

Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs.

no code implementations • 14 Jun 2022 • Beng Chin Ooi, Gang Chen, Mike Zheng Shou, Kian-Lee Tan, Anthony Tung, Xiaokui Xiao, James Wei Luen Yip, Meihui Zhang

In the Metaverse, the physical space and the virtual space co-exist, and interact simultaneously.

no code implementations • 7 Jun 2022 • Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar

We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distributions including Bernoulli, Gaussian, Gamma, Exponential, etc.

no code implementations • Findings (NAACL) 2022 • Juncheng Liu, Zequn Sun, Bryan Hooi, Yiwei Wang, Dayiheng Liu, Baosong Yang, Xiaokui Xiao, Muhao Chen

We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.

1 code implementation • 1 May 2022 • Héber H. Arcolezi, Jean-François Couchot, Denis Renaud, Bechara Al Bouna, Xiaokui Xiao

As shown in the results, differentially private deep learning models trained under gradient or input perturbation achieve nearly the same performance as non-private deep learning models, with loss in performance varying between $0. 57\%$ to $2. 8\%$.

1 code implementation • NeurIPS 2021 • Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao

Motivated by this limitation, we propose a GNN model with infinite depth, which we call Efficient Infinite-Depth Graph Neural Networks (EIGNN), to efficiently capture very long-range dependencies.

no code implementations • 29 Sep 2021 • Ergute Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Hong Meng Tan, Khin Mi Mi Aung

We point out a major challenge in this problem setting: that common mechanisms for enforcing DP in deep learning, which require injecting \textit{real-valued noise}, are fundamentally incompatible with MPC, which exchanges \textit{finite-field integers} among the participants.

1 code implementation • 17 May 2021 • Xinjian Luo, Xiaokui Xiao, Yuncheng Wu, Juncheng Liu, Beng Chin Ooi

InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye.

no code implementations • ICCV 2021 • Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, Brian Y. Lim

We study this risk for image-based model inversion attacks and identified several attack architectures with increasing performance to reconstruct private image data from model explanations.

no code implementations • 7 Feb 2021 • Renchi Yang, Jieming Shi, Yin Yang, Keke Huang, Shiqi Zhang, Xiaokui Xiao

Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes in G into k disjoint clusters, such that nodes within the same cluster share similar topological and attribute characteristics, while those in different clusters are dissimilar.

1 code implementation • 13 Dec 2020 • Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao

We argue that the representation power in unlabelled nodes can be useful for active learning and for further improving performance of active learning for node classification.

1 code implementation • 20 Oct 2020 • Xinjian Luo, Yuncheng Wu, Xiaokui Xiao, Beng Chin Ooi

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other.

no code implementations • 14 Aug 2020 • Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, Beng Chin Ooi

Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other.

2 code implementations • 14 Apr 2020 • Keke Huang, Jing Tang, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, Andrew Lim

In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of $1-\mathrm{e}^{\rho_b(\varepsilon-1)}$, where $\rho_b=1-(1-1/b)^b$ and $\varepsilon \in (0, 1)$ is a user-specified parameter.

Social and Information Networks

2 code implementations • 4 Mar 2020 • Cong Yue, Zhongle Xie, Meihui Zhang, Gang Chen, Beng Chin Ooi, Sheng Wang, Xiaokui Xiao

We establish the worst-case guarantees of each index in terms of these five metrics, and we experimentally evaluate all indexes in a large variety of settings.

Databases

no code implementations • 3 Mar 2020 • Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu

Thompson sampling is one of the most widely used algorithms for many online decision problems, due to its simplicity in implementation and superior empirical performance over other state-of-the-art methods.

no code implementations • 21 Feb 2020 • Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu

In this paper, we show that a variant of ETC algorithm can actually achieve the asymptotic optimality for multi-armed bandit problems as UCB-type algorithms do and extend it to the batched bandit setting.

no code implementations • 19 Feb 2020 • Jieming Shi, Tianyuan Jin, Renchi Yang, Xiaokui Xiao, Yin Yang

Given a graph G and a node u in G, a single source SimRank query evaluates the similarity between u and every node v in G. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which needs to be performed again whenever the graph G changes.

1 code implementation • NeurIPS 2019 • Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen

For PAC problem, we achieve optimal query complexity and use only $O(\log_{\frac{k}{\delta}}^*(n))$ rounds, which matches the lower bound of round complexity, while most of existing works need $\Theta(\log \frac{n}{k})$ rounds.

no code implementations • 6 Sep 2019 • Dumitrel Loghin, Shaofeng Cai, Gang Chen, Tien Tuan Anh Dinh, Feiyi Fan, Qian Lin, Janice Ng, Beng Chin Ooi, Xutao Sun, Quang-Trung Ta, Wei Wang, Xiaokui Xiao, Yang Yang, Meihui Zhang, Zhonghua Zhang

With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management.

Networking and Internet Architecture Databases Distributed, Parallel, and Cluster Computing

no code implementations • 28 Jun 2019 • Ning Wang, Xiaokui Xiao, Yin Yang, Jun Zhao, Siu Cheung Hui, Hyejin Shin, Junbum Shin, Ge Yu

Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.

no code implementations • 17 Jun 2019 • Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S. Bhowmick

Given an input graph G and a node v in G, homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector.

no code implementations • 30 Sep 2018 • Bo Han, Ivor W. Tsang, Xiaokui Xiao, Ling Chen, Sai-fu Fung, Celina P. Yu

PRESTIGE bridges private updates of the primal variable (by private sampling) with the gradual curriculum learning (CL).

2 code implementations • 15 May 2017 • Wei Lu, Xiaokui Xiao, Amit Goyal, Keke Huang, Laks V. S. Lakshmanan

In a recent SIGMOD paper titled "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study", Arora et al. [1] undertake a performance benchmarking study of several well-known algorithms for influence maximization.

Social and Information Networks

no code implementations • AAAI 2017 • Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao

To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence.

no code implementations • 16 Jun 2016 • Thông T. Nguyên, Xiaokui Xiao, Yin Yang, Siu Cheung Hui, Hyejin Shin, Junbum Shin

Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data.

Databases

no code implementations • EMNLP 2016 • Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao

Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task.

no code implementations • Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data 2014 • Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao

Given a dataset D, PRIVBAYES first constructs a Bayesian network N , which (i) provides a succinct model of the correlations among the attributes in D and (ii) allows us to approximate the distribution of data in D using a set P of lowdimensional marginals of D. After that, PRIVBAYES injects noise into each marginal in P to ensure differential privacy, and then uses the noisy marginals and the Bayesian network to construct an approximation of the data distribution in D. Finally, PRIVBAYES samples tuples from the approximate distribution to construct a synthetic dataset, and then releases the synthetic data.

3 code implementations • 3 Apr 2014 • Youze Tang, Xiaokui Xiao, Yanchen Shi

Given a social network G and a constant k, the influence maximization problem asks for k nodes in G that (directly and indirectly) influence the largest number of nodes under a pre-defined diffusion model.

Social and Information Networks Databases

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