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Tyler Wilson

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4 papers

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IJCAI Conference 2022 Conference Paper

DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data

Asadullah Hill Galib
Andrew McDonald
Tyler Wilson
Lifeng Luo
Pang-Ning Tan

Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) with generalized extreme value (GEV) distribution to forecast the block maximum value of a time series. Implementing such a network is a challenge as the framework must preserve the inter-dependent constraints among the GEV model parameters even when the DNN is initialized. We describe our approach to address this challenge and present an architecture that enables both conditional mean and quantile prediction of the block maxima. The extensive experiments performed on both real-world and synthetic data demonstrated the superiority of DeepExtrema compared to other baseline methods.

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AAAI Conference 2022 Conference Paper

DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events

Tyler Wilson
Pang-Ning Tan
Lifeng Luo

Geospatio-temporal data are pervasive across numerous application domains. These rich datasets can be harnessed to predict extreme events such as disease outbreaks, flooding, crime spikes, etc. However, since the extreme events are rare, predicting them is a hard problem. Statistical methods based on extreme value theory provide a systematic way for modeling the distribution of extreme values. In particular, the generalized Pareto distribution (GPD) is useful for modeling the distribution of excess values above a certain threshold. However, applying such methods to large-scale geospatiotemporal data is a challenge due to the difficulty in capturing the complex spatial relationships between extreme events at multiple locations. This paper presents a deep learning framework for long-term prediction of the distribution of extreme values at different locations. We highlight its computational challenges and present a novel framework that combines convolutional neural networks with deep set and GPD. We demonstrate the effectiveness of our approach on a realworld dataset for modeling extreme climate events.

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AAAI Conference 2020 Conference Paper

Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

Farzan Masrour
Tyler Wilson
Heng Yan
Pang-Ning Tan
Abdol Esfahanian

Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as ﬁlter bubble. In this study, we examine the ﬁlter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the ﬁlter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the ﬁlter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i. i. d data.

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IJCAI Conference 2018 Conference Paper

MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling

Jianpeng Xu
Xi Liu
Tyler Wilson
Pang-Ning Tan
Pouyan Hatami
Lifeng Luo

In climate and environmental sciences, vast amount of spatio-temporal data have been generated at varying spatial resolutions from satellite observations and computer models. Integrating such diverse sources of data has proven to be useful for building prediction models as the multi-scale data may capture different aspects of the Earth system. In this paper, we present a novel framework called MUSCAT for predictive modeling of multi-scale, spatio-temporal data. MUSCAT performs a joint decomposition of multiple tensors from different spatial scales, taking into account the relationships between the variables. The latent factors derived from the joint tensor decomposition are used to train the spatial and temporal prediction models at different scales for each location. The outputs from these ensemble of spatial and temporal models will be aggregated to generate future predictions. An incremental learning algorithm is also proposed to handle the massive size of the tensors. Experimental results on real-world data from the United States Historical Climate Network (USHCN) showed that MUSCAT outperformed other competing methods in more than 70\% of the locations.

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