Yatao Bian

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

Achievements

+12 more ↓

🗺️ Taxonomy Completionist (15) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (7)

🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🔬 Deep Specialist (10) 🤝 Dynamic Duo (11) 🏆 Grand Slam 👑 Triple Crown 🗃️ Keyword Collector (123) ❓ The Questioner (2) ⚡ Prolific Year (5) 💎 Century Club (35) 🔥 Unstoppable (7)

Conferences

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

Top co-authors

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

Research topics

Optimization (1)

Keywords

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

Papers

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions ACL 2026 CrystalDiT: Simple Diffusion Transformers for Crystal Generation AAAI 2026 Measuring Diversity in Synthetic Datasets ICML 2025 Hierarchical Graph Tokenization for Molecule-Language Alignment ICML 2025 Erasing Concept Combination from Text-to-Image Diffusion Model ICLR 2025 COME: Test-time Adaption by Conservatively Minimizing Entropy ICLR 2025 RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning ACL 2025 Unified Molecule-Text Language Model with Discrete Token Representation IJCAI 2025 InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization ICLR 2025 The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection NIPS 2024 WatME: Towards Lossless Watermarking Through Lexical Redundancy ACL 2024 How Interpretable Are Interpretable Graph Neural Networks? ICML 2024 Enhancing Neural Subset Selection: Integrating Background Information into Set Representations ICLR 2024 EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model ICLR 2024 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 SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations NIPS 2023 Understanding and Improving Feature Learning for Out-of-Distribution Generalization NIPS 2023 Learning Invariant Molecular Representation in Latent Discrete Space NIPS 2023 Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NIPS 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 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 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