Chenjuan Guo

Orcid: 0000-0002-4516-4637

According to our database1, Chenjuan Guo authored at least 86 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting.
VLDB J., September, 2024

Efficient Stochastic Routing in Path-Centric Uncertain Road Networks.
Proc. VLDB Endow., July, 2024

QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models.
Proc. VLDB Endow., July, 2024

TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods.
Proc. VLDB Endow., May, 2024

Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching-Extended Version.
CoRR, 2024

Air Quality Prediction with Physics-Informed Dual Neural ODEs in Open Systems.
CoRR, 2024

Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning.
CoRR, 2024

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast.
CoRR, 2024

CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching.
CoRR, 2024

FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting.
CoRR, 2024

Orca: Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models.
CoRR, 2024

Efficient Stochastic Routing in Path-Centric Uncertain Road Networks - Extended Version.
CoRR, 2024

ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning.
CoRR, 2024

Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders.
CoRR, 2024

QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models - Extended Version.
CoRR, 2024

A Crystal Knowledge-Enhanced Pre-training Framework for Crystal Property Estimation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2024

Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Routing with Massive Trajectory Data.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

A Unified Replay-Based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Dependency-Aware Differentiable Neural Architecture Search.
Proceedings of the Computer Vision - ECCV 2024, 2024

Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023
Multiple Time Series Forecasting with Dynamic Graph Modeling.
Proc. VLDB Endow., December, 2023

Weakly Guided Adaptation for Robust Time Series Forecasting.
Proc. VLDB Endow., December, 2023

CGF: A Category Guidance Based PM$_{2.5}$ Sequence Forecasting Training Framework.
IEEE Trans. Knowl. Data Eng., October, 2023

MagicScaler: Uncertainty-aware, Predictive Autoscaling.
Proc. VLDB Endow., 2023

AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting.
Proc. ACM Manag. Data, 2023

LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation.
Proc. ACM Manag. Data, 2023

A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction.
CoRR, 2023

LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation - Extended Version.
CoRR, 2023

LightPath: Lightweight and Scalable Path Representation Learning.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022
Context-Aware Path Ranking in Road Networks.
IEEE Trans. Knowl. Data Eng., 2022

A Pattern Discovery Approach to Multivariate Time Series Forecasting.
CoRR, 2022

AutoPINN: When AutoML Meets Physics-Informed Neural Networks.
CoRR, 2022

Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting.
CoRR, 2022

A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis.
CoRR, 2022

Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting-Full Version.
CoRR, 2022

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection - Extended Version.
CoRR, 2022

Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning - Extended Version.
CoRR, 2022

Towards Spatio-Temporal Aware Traffic Time Series Forecasting-Full Version.
CoRR, 2022

Outlier Detection for Streaming Task Assignment in Crowdsourcing.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Weighted Mutual Learning with Diversity-Driven Model Compression.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

RetroGraph: Retrosynthetic Planning with Graph Search.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Towards Spatio- Temporal Aware Traffic Time Series Forecasting.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

2021
AutoCTS: Automated Correlated Time Series Forecasting.
Proc. VLDB Endow., 2021

Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles.
Proc. VLDB Endow., 2021

AutoCTS: Automated Correlated Time Series Forecasting - Extended Version.
CoRR, 2021

Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles - Extended Version.
CoRR, 2021

Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting - Full version.
CoRR, 2021

Unsupervised Path Representation Learning with Curriculum Negative Sampling.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting.
Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

2020
Context-aware, preference-based vehicle routing.
VLDB J., 2020

A Correlated Time Series Forecast System.
Proceedings of the 21st IEEE International Conference on Mobile Data Management, 2020

Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks.
Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

2019
Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories.
Proc. VLDB Endow., 2019

Outlier Detection for Time Series with Recurrent Autoencoder Ensembles.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Stochastic Weight Completion for Road Networks Using Graph Convolutional Networks.
Proceedings of the 35th IEEE International Conference on Data Engineering, 2019

2018
PACE: a PAth-CEntric paradigm for stochastic path finding.
VLDB J., 2018

Risk-aware path selection with time-varying, uncertain travel costs: a time series approach.
VLDB J., 2018

Recurrent Multi-Graph Neural Networks for Travel Cost Prediction.
CoRR, 2018

Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results.
CoRR, 2018

Learning to Route with Sparse Trajectory Sets - Extended Version.
CoRR, 2018

Learning to Route with Sparse Trajectory Sets.
Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

Distinguishing Trajectories from Different Drivers using Incompletely Labeled Trajectories.
Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

Correlated Time Series Forecasting using Multi-Task Deep Neural Networks.
Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

2016
Path Cost Distribution Estimation Using Trajectory Data.
Proc. VLDB Endow., 2016

Finding Frequently Visited Indoor POIs Using Symbolic Indoor Tracking Data.
Proceedings of the 19th International Conference on Extending Database Technology, 2016

2015
Toward personalized, context-aware routing.
VLDB J., 2015

EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data.
GeoInformatica, 2015

Efficient and Accurate Path Cost Estimation Using Trajectory Data.
CoRR, 2015

EcoSky: Reducing vehicular environmental impact through eco-routing.
Proceedings of the 31st IEEE International Conference on Data Engineering, 2015

Personalized route recommendation using big trajectory data.
Proceedings of the 31st IEEE International Conference on Data Engineering, 2015

2014
Towards Total Traffic Awareness.
SIGMOD Rec., 2014

Stochastic skyline route planning under time-varying uncertainty.
Proceedings of the IEEE 30th International Conference on Data Engineering, Chicago, 2014

2013
A Functional Model for Dataspace Management Systems.
Proceedings of the Advanced Query Processing, Volume 1: Issues and Trends, 2013

EvoMatch: An Evolutionary Algorithm for Inferring Schematic Correspondences.
Trans. Large Scale Data Knowl. Centered Syst., 2013

Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models.
Proc. VLDB Endow., 2013

MatchBench: Benchmarking Schema Matching Algorithms for Schematic Correspondences.
Proceedings of the Big Data - 29th British National Conference on Databases, 2013

2012
DSToolkit: An Architecture for Flexible Dataspace Management.
Trans. Large Scale Data Knowl. Centered Syst., 2012

EcoMark: evaluating models of vehicular environmental impact.
Proceedings of the SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), 2012

2011
Inferring information about correspondences between data sources for dataspaces.
PhD thesis, 2011

Pay-as-you-go mapping selection in dataspaces.
Proceedings of the ACM SIGMOD International Conference on Management of Data, 2011

2010
Flexible Dataspace Management Through Model Management.
Proceedings of the 2010 EDBT/ICDT Workshops, Lausanne, Switzerland, March 22-26, 2010, 2010


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