276 citations · 664 across the 33 of their papers we have counts for
35 papers
AdaST: Adaptive Coupling for Spatial-Temporal Forecasting
Zhenyu Lei, Chenghao Liu, Yushun Dong +2
Spatial-temporal (ST) forecasting underpins many real-world systems such as traffic, climate, and energy networks. While existing methods implicitly assume strong spatiotemporal co…
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer
Chenghao Liu, Xiaoyang Qu, Jianzong Wang +1
Federated Learning (FL) has been widely concerned for it enables decentralized learning while ensuring data privacy. However, most existing methods unrealistically assume that the…
PyRCA: A Library for Metric-based Root Cause Analysis
Chenghao Liu, Wenzhuo Yang, Himanshu Mittal +3
We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framew…
OTW: Optimal Transport Warping for Time Series
Fabian Latorre, Chenghao Liu, Doyen Sahoo +1
Dynamic Time Warping (DTW) has become the pragmatic choice for measuring distance between time series. However, it suffers from unavoidable quadratic time complexity when the optim…
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges
Qian Cheng, Doyen Sahoo, Amrita Saha +6
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to…
Unsupervised Brain Tumor Segmentation with Image-based Prompts
Xinru Zhang, Ni Ou, Chenghao Liu +3
Automated brain tumor segmentation based on deep learning (DL) has achieved promising performance. However, it generally relies on annotated images for model training, which is not…
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer
Qiheng Mao, Zemin Liu, Chenghao Liu +1
Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To a…
LogAI: A Library for Log Analytics and Intelligence
Qian Cheng, Amrita Saha, Wenzhuo Yang +3
Software and System logs record runtime information about processes executing within a system. These logs have become the most critical and ubiquitous forms of observability data t…
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Devansh Arpit, Matthew Fernandez, Itai Feigenbaum +11
We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and…
Continual Learning, Fast and Slow
Quang Pham, Chenghao Liu, Steven C. H. Hoi
According to the Complementary Learning Systems (CLS) theory~\cite{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary sys…
Learning Deep Time-index Models for Time Series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo +2
Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models. Yet, despite the attracti…
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank
Mouxiang Chen, Chenghao Liu, Zemin Liu +1
Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH),…
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning
Quang Pham, Chenghao Liu, Steven Hoi
Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of…
Learning Fast and Slow for Online Time Series Forecasting
Quang Pham, Chenghao Liu, Doyen Sahoo +1
The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes…
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo +2
Transformers have been actively studied for time-series forecasting in recent years. While often showing promising results in various scenarios, traditional Transformers are not de…
Node-wise Localization of Graph Neural Networks
Zemin Liu, Yuan Fang, Chenghao Liu +1
Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively a…
DualNet: Continual Learning, Fast and Slow
Quang Pham, Chenghao Liu, Steven Hoi
According to Complementary Learning Systems (CLS) theory~\citep{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary system…
Modeling Dynamic Attributes for Next Basket Recommendation
Yongjun Chen, Jia Li, Chenghao Liu +4
Traditional approaches to next item and next basket recommendation typically extract users' interests based on their past interactions and associated static contextual information…
Merlion: A Machine Learning Library for Time Series
Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu +20
We introduce Merlion, an open-source machine learning library for time series. It features a unified interface for many commonly used models and datasets for anomaly detection and…
CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation
Xinru Zhang, Chenghao Liu, Ni Ou +5
Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data augmentati…
Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling
Jianwen Yin, Chenghao Liu, Weiqing Wang +2
Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising. The performance…
Bilevel Continual Learning
Quang Pham, Doyen Sahoo, Chenghao Liu +1
Continual learning aims to learn continuously from a stream of tasks and data in an online-learning fashion, being capable of exploiting what was learned previously to improve curr…
Adaptive Task Sampling for Meta-Learning
Chenghao Liu, Zhihao Wang, Doyen Sahoo +3
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classif…
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Kaize Ding, Jianling Wang, Jundong Li +3
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central an…