Xinwei Sun

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

Achievements

+12 more ↓

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (8) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (9)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🤝 Dynamic Duo (13) 👑 Triple Crown 🏆 Keyword Champion 💎 Century Club (25) ⚡ Prolific Year (5) 📈 Trend Setter ❓ The Questioner 🗃️ Keyword Collector (91) 🔥 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