103 citations · 154 across the 35 of their papers we have counts for
35 papers
GUIDE: Interpretable GUI Agent Evaluation via Hierarchical Diagnosis
Yuwen Zhai, Runze Li, Liang Wang +6
Evaluating GUI agents presents a distinct challenge: trajectories are long, visually grounded, and open-ended, yet evaluation must be both accurate and interpretable. Existing appr…
Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction
Liang Wang, Shubham Jain, Yingtong Dou +9
Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score r…
Matrix Profile for Anomaly Detection on Multidimensional Time Series
Chin-Chia Michael Yeh, Audrey Der, Uday Singh Saini +11
The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anoma…
Compressing and Interpreting Word Embeddings with Latent Space Regularization and Interactive Semantics Probing
Haoyu Li, Junpeng Wang, Yan Zheng +3
Word embedding, a high-dimensional (HD) numerical representation of words generated by machine learning models, has been used for different natural language processing tasks, e.g.,…
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
Prince Aboagye, Yan Zheng, Junpeng Wang +8
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets. Traditionally, these models are assesse…
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
Chin-Chia Michael Yeh, Yujie Fan, Xin Dai +9
Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world challenges. In this paper, we investigate the potential of addressing spatial-temporal fo…
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
Audrey Der, Chin-Chia Michael Yeh, Yan Zheng +5
In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations…
Sketching Multidimensional Time Series for Fast Discord Mining
Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan +7
Time series discords are a useful primitive for time series anomaly detection, and the matrix profile is capable of capturing discord effectively. There exist many research efforts…
Time Series Synthesis Using the Matrix Profile for Anonymization
Audrey Der, Chin-Chia Michael Yeh, Yan Zheng +6
Publishing and sharing data is crucial for the data mining community, allowing collaboration and driving open innovation. However, many researchers cannot release their data due to…
Ego-Network Transformer for Subsequence Classification in Time Series Data
Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan +8
Time series classification is a widely studied problem in the field of time series data mining. Previous research has predominantly focused on scenarios where relevant or foregroun…
Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights
Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai +8
Time series data is ubiquitous across various domains such as finance, healthcare, and manufacturing, but their properties can vary significantly depending on the domain they origi…
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning
Yiran Li, Junpeng Wang, Prince Aboagye +5
Recent advancements in pre-trained large-scale language-image models have ushered in a new era of visual comprehension, offering a significant leap forward. These breakthroughs hav…
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
Dongyu Zhang, Liang Wang, Xin Dai +7
Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, seq…
Multitask Learning for Time Series Data with 2D Convolution
Chin-Chia Michael Yeh, Xin Dai, Yan Zheng +7
Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally…
An Efficient Content-based Time Series Retrieval System
Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai +9
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, health…
Toward a Foundation Model for Time Series Data
Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen +8
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 adapt…
Revealing the Power of Masked Autoencoders in Traffic Forecasting
Jiarui Sun, Yujie Fan, Chin-Chia Michael Yeh +2
Traffic forecasting, crucial for urban planning, requires accurate predictions of spatial-temporal traffic patterns across urban areas. Existing research mainly focuses on designin…
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding
Yan Zheng, Junpeng Wang, Chin-Chia Michael Yeh +4
Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance re…
Visual Analytics For Machine Learning: A Data Perspective Survey
Junpeng Wang, Shixia Liu, Wei Zhang
The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML…
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Xin Dai, Yujie Fan, Zhongfang Zhuang +7
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories. It has been recognized that lea…
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Yiran Li, Junpeng Wang, Xin Dai +5
Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a…
Dynamic Graph Node Classification via Time Augmentation
Jiarui Sun, Mengting Gu, Chin-Chia Michael Yeh +3
Node classification for graph-structured data aims to classify nodes whose labels are unknown. While studies on static graphs are prevalent, few studies have focused on dynamic gra…
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Prince O Aboagye, Yan Zheng, Michael Yeh +6
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignmen…
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng +6
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer. When applying…