Hengrui Zhang
14 papers · 2021–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (24) π Conference Polyglot (7) π Renaissance Researcher (7) π§ Keyword Pioneer
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Conference Polyglot
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(14)
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Conferences
ICLR (4)
NIPS (4)
ICML (2)
AAAI (1)
ACL (1)
CVPR (1)
EMNLP (1)
Top co-authors
Keywords
graph neural network
(3)
representation learning
(2)
self-supervised learning
(2)
graph representation learning
(2)
contrastive learning
(2)
collaborative filtering
(1)
matrix factorization
(1)
unsupervised learning
(1)
off-policy reinforcement learning
(1)
constrained reinforcement learning
(1)
in-context learning
(1)
information retrieval
(1)
model architecture
(1)
graph representation
(1)
canonical correlation analysis
(1)
large-scale learning
(1)
mutual information
(1)
off-policy learning
(1)
attention mechanism
(1)
retrieval augmented generation
(1)
Papers
TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data
ICML 2025
TABGEN-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
ACL 2025
PathwiseRAG: Multi-Dimensional Exploration and Integration Framework
EMNLP 2025
TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
ICLR 2025
DiffPuter: Empowering Diffusion Models for Missing Data Imputation
ICLR 2025
Enhancing Off-Policy Constrained Reinforcement Learning through Adaptive Ensemble C Estimation
AAAI 2024
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
ICLR 2024
Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference
NIPS 2024
Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering
NIPS 2024
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
NIPS 2023
Align Representations With Base: A New Approach to Self-Supervised Learning
CVPR 2022
Handling Distribution Shifts on Graphs: An Invariance Perspective
ICLR 2022
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
ICML 2021
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
NIPS 2021