Yu-feng Li

Step Back to Leap Forward: Self-Backtracking for Symbolic Reasoning and Planning in Language Models AAAI 2026 AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking EMNLP 2025 CARTS: Advancing Neural Theorem Proving with Diversified Tactic Calibration and Bias-Resistant Tree Search ICLR 2025 EvolveSearch: An Iterative Self-Evolving Search Agent EMNLP 2025 Fully Test-time Adaptation for Tabular Data AAAI 2025 Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models IJCAI 2025 RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models CVPR 2025 TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments ICML 2025 VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning EMNLP 2025 Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible ICML 2025 Vision-Language Model Selection and Reuse for Downstream Adaptation ICML 2025 Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts ICML 2024 DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection ICML 2024 HONGAT: Graph Attention Networks in the Presence of High-Order Neighbors AAAI 2024 Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts ICML 2024 LSPAN: Spectrally Localized Augmentation for Graph Consistency Learning IJCAI 2024 Neuro-Symbolic Data Generation for Math Reasoning NeurIPS 2024 Realistic Evaluation of Semi-supervised Learning Algorithms in Open Environments ICLR 2024 Robust Test-Time Adaptation for Zero-Shot Prompt Tuning AAAI 2024 Safe Abductive Learning in the Presence of Inaccurate Rules AAAI 2024 Vision-Language Models are Strong Noisy Label Detectors NeurIPS 2024 Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions ICML 2023 How Re-sampling Helps for Long-Tail Learning? NeurIPS 2023 Identifying Useful Learnwares for Heterogeneous Label Spaces ICML 2023 ODS: Test-Time Adaptation in the Presence of Open-World Data Shift ICML 2023 Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding ICML 2022 LOG: Active Model Adaptation for Label-Efficient OOD Generalization NeurIPS 2022 Robust Semi-Supervised Learning when Not All Classes have Labels NeurIPS 2022 USB: A Unified Semi-supervised Learning Benchmark for Classification NeurIPS 2022 Abductive Learning with Ground Knowledge Base IJCAI 2021 Dash: Semi-Supervised Learning with Dynamic Thresholding ICML 2021 Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability AAAI 2021 NGC: A Unified Framework for Learning With Open-World Noisy Data ICCV 2021 Safe Weakly Supervised Learning IJCAI 2021 STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data NeurIPS 2021 Towards Robust Model Reuse in the Presence of Latent Domains IJCAI 2021 IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience AAAI 2020 Learning from Weak-Label Data: A Deep Forest Expedition AAAI 2020 Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data ICML 2020 Semi-Supervised Streaming Learning with Emerging New Labels AAAI 2020 Learning Compact Model for Large-Scale Multi-Label Data AAAI 2019 Learning for Tail Label Data: A Label-Specific Feature Approach IJCAI 2019 Partial Label Learning with Unlabeled Data IJCAI 2019 Rapid Performance Gain through Active Model Reuse IJCAI 2019 Towards Automated Semi-Supervised Learning AAAI 2019 Does Tail Label Help for Large-Scale Multi-Label Learning IJCAI 2018 Lightweight Label Propagation for Large-Scale Network Data IJCAI 2018 Graph Quality Judgement: A Large Margin Expedition IJCAI 2016 Convex and Scalable Weakly Labeled SVMs JMLR 2013 Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison NeurIPS 2012 Tighter and Convex Maximum Margin Clustering AISTATS 2005