Search Results for author: Haoyu Liu

Found 9 papers, 4 papers with code

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

1 code implementation28 Jul 2023 Renyu Zhu, Haoyu Liu, Runze Wu, Minmin Lin, Tangjie Lv, Changjie Fan, Haobo Wang

In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise.

Learning with noisy labels

SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation

1 code implementation17 May 2023 Haoyu Liu, Ningyi Liao, Siqiang Luo

Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i. e. neighboring nodes are dissimilar, due to their local and uniform aggregation.

Graph Learning

Detecting Multivariate Time Series Anomalies with Zero Known Label

2 code implementations3 Aug 2022 Qihang Zhou, Jiming Chen, Haoyu Liu, Shibo He, Wenchao Meng

Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required.

Density Estimation Graph structure learning +3

NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks

no code implementations20 Feb 2022 Haoyu Liu, Paul Patras

Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden in data streams.

Data Augmentation Network Intrusion Detection

Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression

no code implementations18 Sep 2021 Haizhou Du, Xiaojie Feng, Qiao Xiang, Haoyu Liu

Specifically, in LGC, local gradients from a device is coded into several layers and each layer is sent to the FL server along a different channel.

Federated Learning

Fairness-aware Outlier Ensemble

no code implementations17 Mar 2021 Haoyu Liu, Fenglong Ma, Shibo He, Jiming Chen, Jing Gao

Meanwhile, we propose a post-processing framework to tune the original ensemble results through a stacking process so that we can achieve a trade off between fairness and detection performance.

Fairness Fraud Detection +1

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