Ying Wei
ZJU-100 Young Professor
Studies compositionality and lifelong learning in LLMs and agents, with a focus on AI for science.
Ying Wei's lab at Zhejiang works in Machine Learning. 37 papers since 2023, 36 at top venues, mostly NeurIPS and ICLR. 13 PhD students and 1 master's student. 5 alumni. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 97%
- PhD students
- 13
- MS students
- 1
- alumni listed
- 5
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 4 · collecting
- 10 papers2025 · T1 10
- 16 papers2024 · T1 15
- 7 papers2023 · T1 7
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, ACL, EMNLP
Research focuspaper titles that use each topic's words
- llms3 papers
- lifelong0 papers
- agents0 papers
- artificial intelligence0 papers
- artificial0 papers
Paper list
From the lab homepage.
20264 papers
- ICML (T1)Concept-Guided Tokenization: Closing the Gap Between Reconstruction and Generation
- ICLR (T1)Plug-and-Play Compositionality for Boosting Continual Learning with Foundation Models
- ICML (T1)RetrOrchestrator: A Multi-Step Retrosynthesis Agent Dynamically Orchestrating Single-Step Transition Models
- ICLR (T1)Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
202510 papers
- NeurIPS (T1)A³E: Towards Compositional Model Editing
- ACL (T1)Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts
- ICLR (T1)CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models
- ICML (T1)Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation
- NeurIPS (T1)Curriculum Model Merging: Harmonizing Chemical LLMs for Enhanced Cross-Task Generalization
- NeurIPS (T1)Data Selection Matters: Towards Robust Instruction Tuning of Large Multimodal Models
- ICML (T1)Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3D Structures
- ICLR (T1)SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
- ICLR (T1)Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning
- EMNLP (T1)What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning
202416 papers
- ICLR (T1)Active Retrosynthetic Planning Aware of Route Quality
- ACL (T1)Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation
- NeurIPS (T1)DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
- ICML (T1)Federated Continual Learning via Prompt-based Dual Knowledge Transfer
- ICLR (T1)Gradual Domain Adaptation via Gradient Flow
- NeurIPS (T1)Learning Where to Edit Vision Transformers
- ICLR (T1)Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction
- ICML (T1)Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations
- EMNLP (T1)Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing
- NeurIPS (T1)Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-tuning
- CVPR (T1)MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric
- ICML (T1)One Meta-tuned Transformer is What You Need for Few-shot Learning
20237 papers
- CVPR (T1)Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
- ICCV (T1)Concept-wise Fine-tuning Matters in Preventing Negative Transfer
- NeurIPS (T1)Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework
- AAAI (T1)Learning Chemical Rules of Retrosynthesis with Pre-training
- ACL (T1)Learning to Substitute Spans towards Improving Compositional Generalization
- NeurIPS (T1)Secure Out-of-Distribution Task Generalization with Energy-Based Models
- EMNLP (T1)Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts
Where alumni went5 listed
Apply to ZhejiangFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- Per ZJU language requirements
- Fee
- RMB 800
Not yet confirmed for this intake.
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