Furui Liu

Orcid: 0000-0003-3997-3822

According to our database1, Furui Liu authored at least 56 papers between 2012 and 2024.

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Bibliography

2024
Team-wise effective communication in multi-agent reinforcement learning.
Auton. Agents Multi Agent Syst., December, 2024

SFANet: Spatial-Frequency Attention Network for Weather Forecasting.
CoRR, 2024

Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning.
CoRR, 2024

CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.
Comput. Biol. Medicine, 2024

ANEDL: Adaptive Negative Evidential Deep Learning for Open-Set Semi-supervised Learning.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

DR-Label: Label Deconstruction and Reconstruction of GNN Models for Catalysis Systems.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Debiased Recommendation with User Feature Balancing.
ACM Trans. Inf. Syst., October, 2023

ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling.
Nat. Mac. Intell., September, 2023

rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations.
Briefings Bioinform., May, 2023

Contrastive-ACE: Domain Generalization Through Alignment of Causal Mechanisms.
IEEE Trans. Image Process., 2023

Efficient and accurate large library ligand docking with KarmaDock.
Nat. Comput. Sci., 2023

CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading.
CoRR, 2023

Invariant Learning via Probability of Sufficient and Necessary Causes.
CoRR, 2023

Meta Adaptive Task Sampling for Few-Domain Generalization.
CoRR, 2023

Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning.
CoRR, 2023

DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction.
CoRR, 2023

Invariant Learning via Probability of Sufficient and Necessary Causes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Robust Classifier for Imbalanced Medical Image Dataset with Noisy Labels by Minimizing Invariant Risk.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Fast Non-Markovian Diffusion Model for Weakly Supervised Anomaly Detection in Brain MR Images.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Specify Robust Causal Representation from Mixed Observations.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Uncertainty Estimation by Fisher Information-based Evidential Deep Learning.
Proceedings of the International Conference on Machine Learning, 2023

CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Traj-MAE: Masked Autoencoders for Trajectory Prediction.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

TieComm: Learning a Hierarchical Communication Topology Based on Tie Theory.
Proceedings of the Database Systems for Advanced Applications, 2023

RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Learning to select cuts for efficient mixed-integer programming.
Pattern Recognit., 2022

Weakly Supervised Disentangled Generative Causal Representation Learning.
J. Mach. Learn. Res., 2022

Structured Q-learning For Antibody Design.
CoRR, 2022

Treatment Effect Estimation with Unmeasured Confounders in Data Fusion.
CoRR, 2022

Generalizable Information Theoretic Causal Representation.
CoRR, 2022

Debiased Recommendation with User Feature Balancing.
CoRR, 2022

Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-Based Policy Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

ConfounderGAN: Protecting Image Data Privacy with Causal Confounder.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2022

2021
Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth.
CoRR, 2021

Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms.
CoRR, 2021

Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

DARING: Differentiable Causal Discovery with Residual Independence.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Causal World Models by Unsupervised Deconfounding of Physical Dynamics.
CoRR, 2020

Disentangled Generative Causal Representation Learning.
CoRR, 2020

Decoder-free Robustness Disentanglement without (Additional) Supervision.
CoRR, 2020

CausalVAE: Structured Causal Disentanglement in Variational Autoencoder.
CoRR, 2020

2018
Causal Inference on Multidimensional Data Using Free Probability Theory.
IEEE Trans. Neural Networks Learn. Syst., 2018

Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures.
Neural Comput., 2018

2017
On the Relations of Theoretical Foundations of Different Causal Inference Algorithms.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2017 - 18th International Conference, Guilin, China, October 30, 2017

2016
Causal Discovery on Discrete Data with Extensions to Mixture Model.
ACM Trans. Intell. Syst. Technol., 2016

Causal Inference on Discrete Data via Estimating Distance Correlations.
Neural Comput., 2016

2013
U-Air: when urban air quality inference meets big data.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

2012
Unsupervised Feature Selection for Multi-cluster Data via Smooth Distributed Score.
Proceedings of the Emerging Intelligent Computing Technology and Applications, 2012

Dual Locality Preserving Nonnegative Matrix Factorization for image analysis.
Proceedings of the 2012 IEEE International Conference on Granular Computing, 2012


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