Xinwei Sun

Orcid: 0000-0001-6962-7985

Affiliations:
  • Fudan University, School of Data Science and MOE Frontiers Center for Brain Science, Shanghai, China
  • Microsoft Research Asia, Beijing, China (2019 - 2022)
  • Peking University, China (PhD 2018)


According to our database1, Xinwei Sun authored at least 48 papers between 2016 and 2024.

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

Timeline

Legend:

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Online presence:

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Bibliography

2024
Diversify: A General Framework for Time Series Out-of-Distribution Detection and Generalization.
IEEE Trans. Pattern Anal. Mach. Intell., June, 2024

Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels.
IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

Robust Network Learning via Inverse Scale Variational Sparsification.
CoRR, 2024

LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion.
CoRR, 2024

Bayesian Intervention Optimization for Causal Discovery.
CoRR, 2024

Causal Discovery via Conditional Independence Testing with Proxy Variables.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Doubly Robust Proximal Causal Learning for Continuous Treatments.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
PatchMix Augmentation to Identify Causal Features in Few-Shot Learning.
IEEE Trans. Pattern Anal. Mach. Intell., June, 2023

Exploring Structural Sparsity of Deep Networks Via Inverse Scale Spaces.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

Exploring Counterfactual Alignment Loss towards Human-centered AI.
CoRR, 2023

Causal Discovery with Unobserved Variables: A Proxy Variable Approach.
CoRR, 2023

Causal Discovery from Subsampled Time Series with Proxy Variables.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Which Invariance Should We Transfer? A Causal Minimax Learning Approach.
Proceedings of the International Conference on Machine Learning, 2023

Learning Domain-Agnostic Representation for Disease Diagnosis.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Out-of-distribution Representation Learning for Time Series Classification.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

A New Causal Decomposition Paradigm towards Health Equity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Sparse Learning in AI: A Differential Inclusion Perspective.
Proceedings of the ACM Turing Award Celebration Conference - China 2023, 2023

2022
Domain Invariant Model with Graph Convolutional Network for Mammogram Classification.
CoRR, 2022

Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification.
IEEE Trans. Image Process., 2021

Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition.
CoRR, 2021

Causally Invariant Predictor with Shift-Robustness.
CoRR, 2021

Recovering Latent Causal Factor for Generalization to Distributional Shifts.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Causal Semantic Representation for Out-of-Distribution Prediction.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

DAE-GCN: Identifying Disease-Related Features for Disease Prediction.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

CA-Net: Leveraging Contextual Features for Lung Cancer Prediction.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Forecasting Irreversible Disease via Progression Learning.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Causal Hidden Markov Model for Time Series Disease Forecasting.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Perturbed Amplitude Flow for Phase Retrieval.
IEEE Trans. Signal Process., 2020

Disease Forecast via Progression Learning.
CoRR, 2020

Identifying Invariant Texture Violation for Robust Deepfake Detection.
CoRR, 2020

Latent Causal Invariant Model.
CoRR, 2020

Learning Causal Semantic Representation for Out-of-Distribution Prediction.
CoRR, 2020

Leveraging both Lesion Features and Procedural Bias in Neuroimaging: An Dual-Task Split dynamics of inverse scale space.
CoRR, 2020

DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths.
Proceedings of the 37th International Conference on Machine Learning, 2020

TCGM: An Information-Theoretic Framework for Semi-supervised Multi-modality Learning.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Parsimonious Deep Learning: A Differential Inclusion Approach with Global Convergence.
CoRR, 2019

S<sup>2</sup>-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning.
CoRR, 2019

Zero-Shot Learning Via Recurrent Knowledge Transfer.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2019

iSplit LBI: Individualized Partial Ranking with Ties via Split LBI.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning With Unsure Data for Medical Image Diagnosis.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
A Margin-based MLE for Crowdsourced Partial Ranking.
Proceedings of the 2018 ACM Multimedia Conference on Multimedia Conference, 2018

FDR-HS: An Empirical Bayesian Identification of Heterogenous Features in Neuroimage Analysis.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit.
CoRR, 2017

GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017

2016
Split LBI: An Iterative Regularization Path with Structural Sparsity.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016


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