no code implementations • 28 Feb 2024 • Xinjian Luo, Yangfan Jiang, Fei Wei, Yuncheng Wu, Xiaokui Xiao, Beng Chin Ooi
We demonstrate that the sharer can execute fairness poisoning attacks to undermine the receiver's downstream models by manipulating the training data distribution of the diffusion model.
no code implementations • 28 Dec 2023 • Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi, Wenqiao Zhang
Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively.
no code implementations • 26 Nov 2023 • Yizheng Zhu, Yuncheng Wu, Zhaojing Luo, Beng Chin Ooi, Xiaokui Xiao
In this paper, we propose a novel and highly efficient solution RiseFL for secure and verifiable data collaboration, ensuring input privacy and integrity simultaneously. Firstly, we devise a probabilistic integrity check method that significantly reduces the cost of ZKP generation and verification.
no code implementations • 16 Oct 2023 • Xiaochen Zhu, Xinjian Luo, Yuncheng Wu, Yangfan Jiang, Xiaokui Xiao, Beng Chin Ooi
SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL.
no code implementations • ICCV 2023 • Wenqiao Zhang, Changshuo Liu, Lingze Zeng, Beng Chin Ooi, Siliang Tang, Yueting Zhuang
Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed.
no code implementations • 10 Apr 2023 • Changshuo Liu, Wenqiao Zhang, Beng Chin Ooi, James Wei Luen Yip, Lingze Zeng, Kaiping Zheng
In this paper, we propose a universal COhort Representation lEarning (CORE) framework to augment EHR utilization by leveraging the fine-grained cohort information among patients.
no code implementations • 30 Mar 2023 • Wenqiao Zhang, Changshuo Liu, Can Cui, Beng Chin Ooi
In this paper, we analyze the SSDA problem from two perspectives that have previously been overlooked, and correspondingly decompose it into two \emph{key subproblems}: \emph{robust domain adaptation (DA) learning} and \emph{maximal cross-domain data utilization}.
no code implementations • 14 Feb 2023 • Zheqi Lv, Zhengyu Chen, Shengyu Zhang, Kun Kuang, Wenqiao Zhang, Mengze Li, Beng Chin Ooi, Fei Wu
The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication.
no code implementations • 9 Feb 2023 • Hexiang Pan, Quang-Trung Ta, Meihui Zhang, Yeow Meng Chee, Gang Chen, Beng Chin Ooi
Consequently, it improves both the response time and throughput, and effectively handles nodes distributed across the Internet where crash and network failures might occur.
no code implementations • 10 Jan 2023 • Kaiping Zheng, Thao Nguyen, Jesslyn Hwei Sing Chong, Charlene Enhui Goh, Melanie Herschel, Hee Hoon Lee, Changshuo Liu, Beng Chin Ooi, Wei Wang, James Yip
In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects.
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 • 12 Sep 2022 • Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang, Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, Fei Wu
DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud.
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.
1 code implementation • CVPR 2022 • Wenqiao Zhang, Lei Zhu, James Hallinan, Andrew Makmur, Shengyu Zhang, Qingpeng Cai, Beng Chin Ooi
In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models: (1) BoostMIS can adaptively leverage the cluster assumption and consistency regularization of the unlabeled data according to the current learning status.
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.
no code implementations • ICLR 2022 • Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low
Recent years have witnessed a surging interest in Neural Architecture Search (NAS).
no code implementations • 3 Aug 2021 • Naili Xing, Sai Ho Yeung, ChengHao Cai, Teck Khim Ng, Wei Wang, Kaiyuan Yang, Nan Yang, Meihui Zhang, Gang Chen, Beng Chin Ooi
Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together.
1 code implementation • 5 Jul 2021 • Shaofeng Cai, Kaiping Zheng, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang
The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature.
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 • 30 Mar 2021 • Can Cui, Wei Wang, Meihui Zhang, Gang Chen, Zhaojing Luo, Beng Chin Ooi
In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes.
no code implementations • 4 Mar 2021 • Yuncheng Wu, Tien Tuan Anh Dinh, Guoyu Hu, Meihui Zhang, Yeow Meng Chee, Beng Chin Ooi
Data scientists today have to manage the end-to-end ML life cycle that includes both model training and model serving, the latter of which is essential, as it makes their works available to end-users.
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 • 17 Oct 2020 • Zhaojing Luo, Sai Ho Yeung, Meihui Zhang, Kaiping Zheng, Lei Zhu, Gang Chen, Feiyi Fan, Qian Lin, Kee Yuan Ngiam, Beng Chin Ooi
In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask.
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.
no code implementations • 24 Mar 2020 • Kaiping Zheng, Shaofeng Cai, Horng Ruey Chua, Wei Wang, Kee Yuan Ngiam, Beng Chin Ooi
In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions.
2 code implementations • 23 Mar 2020 • Pingcheng Ruan, Dumitrel Loghin, Quang-Trung Ta, Meihui Zhang, Gang Chen, Beng Chin Ooi
For evaluation, we implement our method in two blockchains respectively, FabricSharp on top of Hyperledger Fabric, and FastFabricSharp on top of FastFabric.
Distributed, Parallel, and Cluster Computing Databases Performance
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 • ICLR 2020 • Shaofeng Cai, Yao Shu, Wei Wang, Gang Chen, Beng Chin Ooi
Recent years have witnessed growing interests in designing efficient neural networks and neural architecture search (NAS).
1 code implementation • 3 Oct 2019 • Pingcheng Ruan, Gang Chen, Tien Tuan Anh Dinh, Qian Lin, Dumitrel Loghin, Beng Chin Ooi, Meihui Zhang
As blockchain evolves into another data management system, the natural question is how it compares against distributed database systems.
Databases Performance
no code implementations • 25 Sep 2019 • Lei Zhu, Wei Wang, Mei Hui Zhang, Beng Chin Ooi, Chang Yao
State-of-the-art Unsupervised Domain Adaptation (UDA) methods learn transferable features by minimizing the feature distribution discrepancy between the source and target domains.
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 • 21 Jun 2019 • Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Kian-Lee Tan
Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition.
2 code implementations • 16 May 2019 • Dumitrel Loghin, Gang Chen, Tien Tuan Anh Dinh, Beng Chin Ooi, Yong Meng Teo
Motivated by the massive energy usage of blockchain, on the one hand, and by significant performance improvements in low-power, wimpy systems, on the other hand, we perform an in-depth time-energy analysis of blockchain systems on low-power nodes in comparison to high-performance nodes.
Distributed, Parallel, and Cluster Computing Databases Emerging Technologies Performance
no code implementations • 13 May 2019 • Shaofeng Cai, Yao Shu, Wei Wang, Beng Chin Ooi
The deployment of deep neural networks in real-world applications is mostly restricted by their high inference costs.
no code implementations • 6 Apr 2019 • Shaofeng Cai, Yao Shu, Gang Chen, Beng Chin Ooi, Wei Wang, Meihui Zhang
However, many recent works show that the standard dropout is ineffective or even detrimental to the training of CNNs.
1 code implementation • 3 Apr 2019 • Shaofeng Cai, Gang Chen, Beng Chin Ooi, Jinyang Gao
Model slicing could be viewed as an elastic computation solution without requiring more computational resources.
1 code implementation • 6 Jun 2018 • Xiangrui Cai, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, Ying Zhang, Xiaojie Yuan
Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics.
no code implementations • 28 May 2018 • Liangqu Long, Wei Wang, Jun Wen, Meihui Zhang, Qian Lin, Beng Chin Ooi
Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task.
no code implementations • 26 Apr 2018 • Jinyang Gao, Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Guoliang Li, Teck Khim Ng, Beng Chin Ooi, Sheng Wang, Jingren Zhou
In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.
1 code implementation • PVLDB (The Proceedings of the VLDB Endowment) 2018 • Wei Wang, Sheng Wang, Jinyang Gao, Meihui Zhang, Gang Chen, Teck Khim Ng, Beng Chin Ooi
Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users.
3 code implementations • 2 Apr 2018 • Hung Dang, Tien Tuan Anh Dinh, Dumitrel Loghin, Ee-Chien Chang, Qian Lin, Beng Chin Ooi
In this work, we take a principled approach to apply sharding, which is a well-studied and proven technique to scale out databases, to blockchain systems in order to improve their transaction throughput at scale.
Distributed, Parallel, and Cluster Computing Cryptography and Security Databases
no code implementations • 14 Feb 2018 • Sheng Wang, Tien Tuan Anh Dinh, Qian Lin, Zhongle Xie, Meihui Zhang, Qingchao Cai, Gang Chen, Wanzeng Fu, Beng Chin Ooi, Pingcheng Ruan
By integrating the core application properties into the storage, ForkBase not only delivers high performance but also reduces development effort.
Databases Cryptography and Security Distributed, Parallel, and Cluster Computing
1 code implementation • 17 Aug 2017 • Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang
Blockchain technologies are gaining massive momentum in the last few years.
Databases Cryptography and Security
2 code implementations • 12 Mar 2017 • Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan
However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other.
Databases Cryptography and Security Distributed, Parallel, and Cluster Computing
no code implementations • 25 Mar 2016 • Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang
The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets.
no code implementations • 12 Dec 2015 • Jinyang Gao, H. V. Jagadish, Beng Chin Ooi
Recent years have witnessed amazing outcomes from "Big Models" trained by "Big Data".