Yu-feng Li

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