276 citations · 664 across the 33 of their papers we have counts for
19 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
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…
Feature Interaction-aware Graph Neural Networks
Kaize Ding, Yichuan Li, Jundong Li +2
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on…
Malicious URL Detection using Machine Learning: A Survey
Doyen Sahoo, Chenghao Liu, Steven C. H. Hoi
Malicious URL, a.k.a. malicious website, is a common and serious threat to cybersecurity. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure…
SOL: A Library for Scalable Online Learning Algorithms
Yue Wu, Steven C. H. Hoi, Chenghao Liu +3
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regula…
Online Bayesian Collaborative Topic Regression
Chenghao Liu, Tao Jin, Steven C. H. Hoi +2
Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (e.g., LDA) for recommender systems, which has gained increasing…