← Authors

Xinwei Sun

25 papers · 2016–2025 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) πŸƒ Academic Marathon (9) πŸ—ΊοΈ Taxonomy Completionist (41) 🌈 Renaissance Researcher (8) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ’Ž Century Club (25) ⚑ Prolific Year (5) πŸ“ˆ Trend Setter ❓ The Questioner πŸ—ƒοΈ Keyword Collector (91) πŸ”₯ Unstoppable (8)

Conferences

ICML (6) ICLR (5) NIPS (5) CVPR (4) AISTATS (2) ECCV (1) ICCV (1) UAI (1)

Top co-authors

Yizhou Wang (13) Yanwei Fu (6) Mingzhou Liu (5) Botong Wu (4) Yuan Yao (4) Lingjing Hu (4) Jing Li (3) Xiangyu Zheng (3) Tie-yan Liu (2) Yu Qiao (2)

Keywords

causal inference (3) time series (2) generative model (2) domain generalization (2) spurious correlation (2) causal discovery (2) structural sparsity (2) disease forecasting (2) causal invariance (2) variable splitting (2) medical imaging (2) variational inference (2) feature selection (1) anomaly detection (1) few-shot learning (1) transfer learning (1) feature learning (1) ordinal regression (1) retinal image (1) semi-supervised learning (1)

Papers

Adaptive Pruning of Pretrained Transformer via Differential Inclusions ICLR 2025
A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging Analysis AISTATS 2025
Bayesian Active Learning for Bivariate Causal Discovery ICML 2025
Bivariate Causal Discovery with Proxy Variables: Integral Solving and Beyond ICML 2025
Learning Causal Alignment for Reliable Disease Diagnosis ICLR 2025
Exploring High-dimensional Search Space via Voronoi Graph Traversing UAI 2024
Causal Discovery via Conditional Independence Testing with Proxy Variables ICML 2024
Doubly Robust Proximal Causal Learning for Continuous Treatments ICLR 2024
Causal Discovery from Subsampled Time Series with Proxy Variables NIPS 2023
Out-of-distribution Representation Learning for Time Series Classification ICLR 2023
Which Invariance Should We Transfer? A Causal Minimax Learning Approach ICML 2023
Learning Domain-Agnostic Representation for Disease Diagnosis ICLR 2023
A New Causal Decomposition Paradigm towards Health Equity AISTATS 2023
Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels CVPR 2022
Forecasting Irreversible Disease via Progression Learning CVPR 2021
Causal Hidden Markov Model for Time Series Disease Forecasting CVPR 2021
Learning Causal Semantic Representation for Out-of-Distribution Prediction NIPS 2021
Recovering Latent Causal Factor for Generalization to Distributional Shifts NIPS 2021
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning ECCV 2020
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths ICML 2020
Learning With Unsure Data for Medical Image Diagnosis ICCV 2019
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI NIPS 2019
Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms CVPR 2019
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning ICML 2018
Split LBI: An Iterative Regularization Path with Structural Sparsity NIPS 2016