Liang Sun
34 papers · 2008–2026 · 10 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (14) π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (5) π£ Hot Topic Early Bird
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Academic Marathon
(17)
πΊοΈ
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(14)
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Keyword Trendsetter Combo
(6)
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Dynamic Duo
(14)
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Topic Pioneer
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(2)
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Unstoppable
(5)
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Conference Pioneer
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Keyword Collector
(144)
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Prolific Year
(6)
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Century Club
(33)
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Conferences
NIPS (10)
IJCAI (7)
AAAI (6)
ICML (4)
ICLR (2)
ACL (1)
CVPR (1)
EMNLP (1)
ICCV (1)
IJCNLP (1)
Top co-authors
Keywords
time series forecasting
(7)
anomaly detection
(4)
convex optimization
(3)
time series
(2)
seasonal-trend decomposition
(2)
representation learning
(2)
multiple kernel learning
(2)
functional connectivity
(2)
few-shot learning
(2)
concept drift
(2)
deep learning
(2)
feature selection
(2)
sparse regularization
(2)
demand forecasting
(2)
reinforcement learning
(2)
transfer learning
(1)
variational inference
(1)
domain generalization
(1)
causal inference
(1)
time series analysis
(1)
Papers
MoCast: Learning Turbulent Motions Under Physical Guidance for Precipitation Nowcasting
AAAI 2026
SCNNs: Spike-based Coupling Neural Networks for Understanding Structural-Functional Relationships in the Human Brain
IJCAI 2025
Learning to Extrapolate and Adjust: Two-Stage Meta-Learning for Concept Drift in Online Time Series Forecasting
IJCAI 2025
Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey
IJCAI 2025
A Non-isotropic Time Series Diffusion Model with Moving Average Transitions
ICML 2025
DeeperForward: Enhanced Forward-Forward Training for Deeper and Better Performance
ICLR 2025
WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction
IJCAI 2024
Task-oriented Time Series Imputation Evaluation via Generalized Representers
NIPS 2024
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
CVPR 2024
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
ICLR 2024
BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
ICML 2024
Explain Temporal Black-Box Models via Functional Decomposition
ICML 2024
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
AAAI 2023
Transformers in Time Series: A Survey
IJCAI 2023
eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms
AAAI 2023
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
NIPS 2023
One Fits All: Power General Time Series Analysis by Pretrained LM
NIPS 2023
A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data
AAAI 2022
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
NIPS 2022
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
NIPS 2022
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
ICML 2022
Time Series Data Augmentation for Deep Learning: A Survey
IJCAI 2021
Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach
NIPS 2021
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
IJCAI 2019
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
AAAI 2019
Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style
ICCV 2019
Which Factorization Machine Modeling Is Better: A Theoretical Answer with Optimal Guarantee
AAAI 2019
Parse Imputation for Dependency Annotations
IJCNLP 2015
Parse Imputation for Dependency Annotations
ACL 2015
Parsing low-resource languages using Gibbs sampling for PCFGs with latent annotations
EMNLP 2014
Projection onto A Nonnegative Max-Heap
NIPS 2011
Efficient Recovery of Jointly Sparse Vectors
NIPS 2009
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
NIPS 2009
Multi-label Multiple Kernel Learning
NIPS 2008