Search Results for author: Yujie Fan

Found 24 papers, 6 papers with code

Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction

no code implementations6 Sep 2024 Liang Wang, Shubham Jain, Yingtong Dou, Junpeng Wang, Chin-Chia Michael Yeh, Yujie Fan, Prince Aboagye, Yan Zheng, Xin Dai, Zhongfang Zhuang, Uday Singh Saini, Wei zhang

Our findings underscore the significance of individual user tastes in the context of online product rating prediction and the robustness of our approach across different model architectures.

Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach

no code implementations2 Jan 2024 Prince Aboagye, Yan Zheng, Junpeng Wang, Uday Singh Saini, Xin Dai, Michael Yeh, Yujie Fan, Zhongfang Zhuang, Shubham Jain, Liang Wang, Wei zhang

The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets.

Ego-Network Transformer for Subsequence Classification in Time Series Data

no code implementations5 Nov 2023 Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan, Xin Dai, Yan Zheng, Vivian Lai, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei zhang, Eamonn Keogh

The ego-networks of all subsequences collectively form a time series subsequence graph, and we introduce an algorithm to efficiently construct this graph.

Time Series Time Series Classification

Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights

no code implementations5 Nov 2023 Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng, Yujie Fan, Vivian Lai, Junpeng Wang, Audrey Der, Zhongfang Zhuang, Liang Wang, Wei zhang

To facilitate this investigation, we introduce a CTSR benchmark dataset that comprises time series data from a variety of domains, such as motion, power demand, and traffic.

Retrieval Time Series +1

An Efficient Content-based Time Series Retrieval System

no code implementations5 Oct 2023 Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng, Junpeng Wang, Vivian Lai, Yujie Fan, Audrey Der, Zhongfang Zhuang, Liang Wang, Wei zhang, Jeff M. Phillips

A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing.

Information Retrieval Retrieval +1

Toward a Foundation Model for Time Series Data

no code implementations5 Oct 2023 Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang

A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks.

Self-Supervised Learning Time Series

Revealing the Power of Masked Autoencoders in Traffic Forecasting

1 code implementation26 Sep 2023 Jiarui Sun, Yujie Fan, Chin-Chia Michael Yeh, Wei zhang, Girish Chowdhary

Our results on traffic benchmarks show that STMAE can largely enhance the forecasting capabilities of various spatial-temporal models.

Decoder Multivariate Time Series Forecasting +2

Adversarial Collaborative Filtering for Free

no code implementations20 Aug 2023 Huiyuan Chen, Xiaoting Li, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Mahashweta Das, Hao Yang

In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer.

Collaborative Filtering

EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding

no code implementations2 Aug 2023 Yan Zheng, Junpeng Wang, Chin-Chia Michael Yeh, Yujie Fan, Huiyuan Chen, Liang Wang, Wei zhang

The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities.

Denoising

PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs

no code implementations2 Jun 2023 Xin Dai, Yujie Fan, Zhongfang Zhuang, Shubham Jain, Chin-Chia Michael Yeh, Junpeng Wang, Liang Wang, Yan Zheng, Prince Osei Aboagye, Wei zhang

Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories.

Contrastive Learning

Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

1 code implementation19 Nov 2022 Mingxuan Ju, Yujie Fan, Chuxu Zhang, Yanfang Ye

Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model.

Product Recommendation

Boosting Graph Neural Networks via Adaptive Knowledge Distillation

no code implementations12 Oct 2022 Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh Chawla

In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN.

Graph Classification Graph Mining +3

Black-box Node Injection Attack for Graph Neural Networks

no code implementations18 Feb 2022 Mingxuan Ju, Yujie Fan, Yanfang Ye, Liang Zhao

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommendation and traffic forecasting.

Product Recommendation

Adaptive Kernel Graph Neural Network

1 code implementation8 Dec 2021 Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Liang Zhao, Yanfang Ye

To solve this problem, in this paper, we propose a novel framework - i. e., namely Adaptive Kernel Graph Neural Network (AKGNN) - which learns to adapt to the optimal graph kernel in a unified manner at the first attempt.

Graph Neural Network Representation Learning

Heterogeneous Temporal Graph Neural Network

1 code implementation26 Oct 2021 Yujie Fan, Mingxuan Ju, Chuxu Zhang, Liang Zhao, Yanfang Ye

To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG.

Graph Neural Network Relation +1

heterogeneous temporal graph transformer: an intelligent system for evolving android malware detection

1 code implementation KDD 2021 Yujie Fan, Mingxuan Ju, Shifu Hou, Yanfang Ye, Wenqiang Wan, Kui Wang, Yinming Mei, Qi Xiong

To capture malware evolution, we further consider the temporal dependence and introduce a heterogeneous temporal graph to jointly model malware propagation and evolution by considering heterogeneous spatial dependencies with temporal dimensions.

Android Malware Detection Malware Detection +2

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