74 citations · 272 across the 31 of their papers we have counts for
11 papers · 1 filter
Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
Tianxiang Zhao, Wenchao Yu, Suhang Wang +6
Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations. However, exi…
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis
Dongjie Wang, Zhengzhang Chen, Yanjie Fu +2
The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system fail…
Personalized Federated Learning under Mixture of Distributions
Yue Wu, Shuaicheng Zhang, Wenchao Yu +5
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while mai…
Time Series Contrastive Learning with Information-Aware Augmentations
Dongsheng Luo, Wei Cheng, Yingheng Wang +8
Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been l…
Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification
Dongbo Xi, Fuzhen Zhuang, Yanchi Liu +4
Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Int…
Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification
Dongbo Xi, Fuzhen Zhuang, Yanchi Liu +3
Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e.g., geolocation i…
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Yinjun Wu, Jingchao Ni, Wei Cheng +7
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging…
T-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Denghui Zhang, Yanchi Liu, Wei Cheng +5
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional tur…
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
Jingjing Gu, Qiang Zhou, Jingyuan Yang +4
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems c…
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
Weijia Zhang, Hao Liu, Yanchi Liu +2
The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction…
Detecting Blackholes and Volcanoes in Directed Networks
Zhongmou Li, Hui Xiong, Yanchi Liu
In this paper, we formulate a novel problem for finding blackhole and volcano patterns in a large directed graph. Specifically, a blackhole pattern is a group which is made of a se…