Search Results for author: Beng Chin Ooi

Found 45 papers, 15 papers with code

METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

no code implementations28 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.

Anomaly Detection Decision Making

Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs

no code implementations26 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.

Federated Learning

Passive Inference Attacks on Split Learning via Adversarial Regularization

no code implementations16 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.

Federated Learning

Toward Cohort Intelligence: A Universal Cohort Representation Learning Framework for Electronic Health Record Analysis

no code implementations10 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.

Representation Learning

CAusal and collaborative proxy-tasKs lEarning for Semi-Supervised Domain Adaptation

no code implementations30 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}.

Domain Adaptation Semi-supervised Domain Adaptation

IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System

no code implementations14 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.

Recommendation Systems Vocal Bursts Intensity Prediction

FLAC: A Robust Failure-Aware Atomic Commit Protocol for Distributed Transactions

no code implementations9 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.

DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization

1 code implementation12 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.

Device-Cloud Collaboration Domain Adaptation +3

BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation

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.

Active Learning

Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy

no code implementations29 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.

Federated Learning Math +1

SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis

no code implementations3 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.

Image Classification

ARM-Net: Adaptive Relation Modeling Network for Structured Data

1 code implementation5 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.

Attribute Decision Making +1

A Fusion-Denoising Attack on InstaHide with Data Augmentation

1 code implementation17 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.

Data Augmentation Denoising

AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment

no code implementations30 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.

AutoML Stock Prediction

Serverless Data Science -- Are We There Yet? A Case Study of Model Serving

no code implementations4 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.


Feature Inference Attack on Model Predictions in Vertical Federated Learning

1 code implementation20 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.

Federated Learning Inference Attack

MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines

no code implementations17 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.

BIG-bench Machine Learning Management

Privacy Preserving Vertical Federated Learning for Tree-based Models

no code implementations14 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.

Federated Learning Privacy Preserving

TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications

no code implementations24 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.

Feature Importance Management +2

A Transactional Perspective on Execute-order-validate Blockchains

2 code implementations23 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

Analysis of Indexing Structures for Immutable Data

2 code implementations4 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.


ISBNet: Instance-aware Selective Branching Networks

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).

Neural Architecture Search

Blockchains vs. Distributed Databases: Dichotomy and Fusion

1 code implementation3 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

Distribution Matching Prototypical Network for Unsupervised Domain Adaptation

no code implementations25 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.

Unsupervised Domain Adaptation

The Disruptions of 5G on Data-driven Technologies and Applications

no code implementations6 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

Database Meets Deep Learning: Challenges and Opportunities

no code implementations21 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.

Image Classification speech-recognition +1

Blockchain Goes Green? An Analysis of Blockchain on Low-Power Nodes

2 code implementations16 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

Dynamic Routing Networks

no code implementations13 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.

Neural Architecture Search

Effective and Efficient Dropout for Deep Convolutional Neural Networks

no code implementations6 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.


Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints

1 code implementation3 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.

Model Compression

Medical Concept Embedding with Time-Aware Attention

1 code implementation6 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.


PANDA: Facilitating Usable AI Development

no code implementations26 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.

Autonomous Driving

Rafiki: Machine Learning as an Analytics Service System

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.

BIG-bench Machine Learning Hyperparameter Optimization +2

Towards Scaling Blockchain Systems via Sharding

3 code implementations2 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

ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications

no code implementations14 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

Untangling Blockchain: A Data Processing View of Blockchain Systems

1 code implementation17 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

BLOCKBENCH: A Framework for Analyzing Private Blockchains

2 code implementations12 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

Deep Learning At Scale and At Ease

no code implementations25 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.

Image Classification

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