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Boyang Liu

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4 papers

2 author rows

AAAI Conference 2022 Conference Paper

Unsupervised Anomaly Detection by Robust Density Estimation

Boyang Liu
Pang-Ning Tan
Jiayu Zhou

Density estimation is a widely used method for unsupervised anomaly detection. However, the presence of anomalies in training data may severely impact the density estimation process, thereby hampering the use of more sophisticated density estimation methods such as those based on deep neural networks. In this work, we propose RobustRealNVP, a robust deep density estimation framework for unsupervised anomaly detection. Our approach differs from existing flowbased models from two perspectives. First, RobustRealNVP discards data points with low estimated densities during optimization to prevent them from corrupting the density estimation process. Furthermore, it imposes Lipschitz regularization to ensure smoothness in the estimated density function. We demonstrate the robustness of our algorithm against anomalies in training data from both theoretical and empirical perspectives. The results show that our algorithm outperforms state-of-the-art unsupervised anomaly detection methods.

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ICML Conference 2021 Conference Paper

Learning Deep Neural Networks under Agnostic Corrupted Supervision

Boyang Liu
Mengying Sun
Ding Wang
Pang-Ning Tan
Jiayu Zhou

Training deep neural network models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact generalization performance. To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption and provides a unified framework for both classification and regression problems. Unlike many existing approaches that quantify the quality of the data points (e. g. , based on their individual loss values), and filter them accordingly, the proposed algorithm focuses on controlling the collective impact of data points on the average gradient. Even when a corrupted data point failed to be excluded by our algorithm, the data point will have a very limited impact on the overall loss, as compared with state-of-the-art filtering methods based on loss values. Extensive experiments on multiple benchmark datasets have demonstrated the robustness of our algorithm under different types of corruption. Our code is available at \url{https: //github. com/illidanlab/PRL}.

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IJCAI Conference 2021 Conference Paper

RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

Boyang Liu
Ding Wang
Kaixiang Lin
Pang-Ning Tan
Jiayu Zhou

Unsupervised anomaly detection plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interests in applying deep neural networks (DNNs) to anomaly detection problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier scores to detect the anomalies. However, due to the high complexity brought upon by the over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Our experimental results also show the resiliency of the framework to missing values compared to other baseline methods.

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AAAI Conference 2020 Conference Paper

OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting

Ding Wang
Boyang Liu
Pang-Ning Tan
Lifeng Luo

Hurricanes are powerful tropical cyclones with sustained wind speeds ranging from at least 74 mph (for category 1 storms) to more than 157 mph (for category 5 storms). Accurate prediction of the storm tracks is essential for hurricane preparedness and mitigation of storm impacts. In this paper, we cast the hurricane trajectory forecasting task as an online multi-lead time location prediction problem and present a framework called OMuLeT to improve path prediction by combining the 6-hourly and 12-hourly forecasts generated from an ensemble of dynamical (physical) hurricane models. OMuLeT employs an online learning with restart strategy to incrementally update the weights of the ensemble model combination as new observation data become available. It can also handle the varying dynamical models available for predicting the trajectories of different hurricanes. Experimental results using the Atlantic and Eastern Paciﬁc hurricane data showed that OMuLeT signiﬁcantly outperforms various baseline methods, including the ofﬁcial forecasts produced by the U. S. National Hurricane Center (NHC), by more than 10% in terms of its 48-hour lead time forecasts.

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