Yatao Bian

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