← Authors

Yatao Bian

36 papers · 2019–2026 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (6) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (15) πŸ—ΊοΈ Taxonomy Completionist (15) 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ”¬ Deep Specialist (10) 🀝 Dynamic Duo (11) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (123) ❓ The Questioner (2) ⚑ Prolific Year (5) πŸ’Ž Century Club (35) πŸ”₯ Unstoppable (7)

Conferences

NIPS (11) ICLR (10) ICML (6) IJCAI (3) AAAI (2) ACL (2) EMNLP (2)

Top co-authors

Peilin Zhao (12) Tingyang Xu (9) Yu Rong (8) Yongqiang Chen (7) Junzhou Huang (7) James Cheng (7) Bingzhe Wu (7) Bo Han (6) Andreas Krause (4) Liang Chen (4)

Research topics

Optimization (1)

Keywords

graph neural network (9) domain generalization (7) out-of-distribution generalization (4) large language model (3) graph representation learning (2) uncertainty quantification (2) message passing (2) variational inference (2) submodular optimization (2) transfer learning (2) invariant learning (2) graph representation (2) representation learning (2) molecular representation learning (2) graph classification (1) few-shot learning (1) bayesian inference (1) domain adaptation (1) adversarial robustness (1) causal inference (1)

Papers

CrystalDiT: Simple Diffusion Transformers for Crystal Generation AAAI 2026
RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning ACL 2025
COME: Test-time Adaption by Conservatively Minimizing Entropy ICLR 2025
Hierarchical Graph Tokenization for Molecule-Language Alignment ICML 2025
Unified Molecule-Text Language Model with Discrete Token Representation IJCAI 2025
InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization ICLR 2025
Measuring Diversity in Synthetic Datasets ICML 2025
Erasing Concept Combination from Text-to-Image Diffusion Model ICLR 2025
EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model ICLR 2024
How Interpretable Are Interpretable Graph Neural Networks? ICML 2024
The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection NIPS 2024
WatME: Towards Lossless Watermarking Through Lexical Redundancy ACL 2024
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations ICLR 2024
Learning Invariant Molecular Representation in Latent Discrete Space NIPS 2023
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators EMNLP 2023
RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data EMNLP 2023
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NIPS 2023
Understanding and Improving Feature Learning for Out-of-Distribution Generalization NIPS 2023
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations NIPS 2023
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization ICLR 2023
BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion ICLR 2023
DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations AAAI 2023
Fairness-guided Few-shot Prompting for Large Language Models NIPS 2023
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking ICLR 2022
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs NIPS 2022
Learning Neural Set Functions Under the Optimal Subset Oracle NIPS 2022
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup NIPS 2022
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning ICLR 2022
$p$-Laplacian Based Graph Neural Networks ICML 2022
Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport IJCAI 2022
On Self-Distilling Graph Neural Network IJCAI 2021
Not All Low-Pass Filters are Robust in Graph Convolutional Networks NIPS 2021
Graph Information Bottleneck for Subgraph Recognition ICLR 2021
Self-Supervised Graph Transformer on Large-Scale Molecular Data NIPS 2020
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models ICML 2020
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference ICML 2019