← Authors

Qitian Wu

29 papers · 2019–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (15) 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (6) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (40) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🀝 Dynamic Duo (20) πŸ† Grand Slam 🧬 Topic Evolution ⚑ Prolific Year (7) πŸ—ƒοΈ Keyword Collector (80) πŸ’Ž Century Club (29) πŸ”₯ Unstoppable (5)

Conferences

NIPS (11) ICLR (7) ICML (7) IJCAI (2) AAAI (1) JMLR (1)

Top co-authors

Junchi Yan (20) Chenxiao Yang (11) Hengrui Zhang (6) Xiaofeng Gao (4) David Wipf (4) Fan Nie (3) Yiting Chen (3) Hongyuan Zha (3) Guihai Chen (3) Wentao Zhao (3)

Keywords

graph neural network (7) collaborative filtering (3) domain generalization (3) distribution shift (3) representation learning (2) self-supervised learning (2) node classification (2) variational inference (2) inductive learning (2) out-of-distribution generalization (2) generative model (2) message passing (2) multi-task learning (1) text classification (1) matrix factorization (1) zero-shot learning (1) sequence generation (1) domain adaptation (1) energy minimization (1) transfer learning (1)

Papers

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation ICLR 2025
DiffPuter: Empowering Diffusion Models for Missing Data Imputation ICLR 2025
Transformers from Diffusion: A Unified Framework for Neural Message Passing JMLR 2025
TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data ICML 2025
Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning ICML 2025
Regularizing Energy among Training Samples for Out-of-Distribution Generalization ICLR 2025
Supercharging Graph Transformers with Advective Diffusion ICML 2025
How Graph Neural Networks Learn: Lessons from Training Dynamics ICML 2024
TDeLTA: A Light-Weight and Robust Table Detection Method Based on Learning Text Arrangement AAAI 2024
Learning Divergence Fields for Shift-Robust Graph Representations ICML 2024
Graph Out-of-Distribution Detection Goes Neighborhood Shaping ICML 2024
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs ICLR 2023
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift NIPS 2023
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations NIPS 2023
Energy-based Out-of-Distribution Detection for Graph Neural Networks ICLR 2023
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion ICLR 2023
Learning Substructure Invariance for Out-of-Distribution Molecular Representations NIPS 2022
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs NIPS 2022
Handling Distribution Shifts on Graphs: An Invariance Perspective ICLR 2022
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach IJCAI 2022
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks NIPS 2022
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification NIPS 2022
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment NIPS 2022
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks NIPS 2021
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach ICML 2021
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach NIPS 2021
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators NIPS 2021
Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust IJCAI 2019
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling NIPS 2019