Sinno Jialin Pan
Professor
Works on transfer learning, domain adaptation and machine learning applications.
Sinno Jialin Pan's lab at CUHK works in Machine Learning. 49 papers since 2023, 34 at top venues, mostly ICLR and NeurIPS. 7 PhD students. 14 alumni. Applications close .
At a glance
- papers, 2023–2026
- 13/ year
- at T1 venues
- 69%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 14
Papers by yearfrom the lab homepage, 2023–2026
- 16 papers2026 · T1 12 · collecting
- 11 papers2025 · T1 7
- 12 papers2024 · T1 7
- 10 papers2023 · T1 8
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, ACL, EMNLP
Research focuspaper titles that use each topic's words
- domain6 papers
- domain adaptation4 papers
- adaptation4 papers
- transfer1 paper
- transfer learning0 papers
Paper list
From the lab homepage.
202616 papers
- NeurIPS (T1)A Theoretical Analysis of Backdoor Learning as Simplicity-Biased Optimization Dynamics
- ACL (T1)Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (opens in a new tab)
- ICLR (T1)Beyond Speedup - Utilizing KV Cache for Sampling and Reasoning (opens in a new tab)
- IEEE Transactions on Artificial IntelligenceBoosting Time-series Domain Adaptation via A Time-Frequency Consensus Framework (opens in a new tab)
- ICLR (T1)Bridging Successor Measures and Online Policy Learning with Flow Matching-Based Representations (opens in a new tab)
- ICML (T1)Deep Coupling Learning for Solving PDEs (opens in a new tab)
- ICLR (T1)Diverse Text Decoding via Iterative Reweighting (opens in a new tab)
- ICLR (T1)Global and Local Topology-Aware Graph Generation via Dual Conditioning Diffusion (opens in a new tab)
- Artificial Intelligence (T1)Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization (opens in a new tab)
- NeurIPS (T1)MemDLM: Memory-Enhanced DLM Training
- ICML (T1)MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification (opens in a new tab)
- Analytical ChemistryNeural Network Guided Global Least Squares for Quantitative Biochemical Component Analysis in Raman Spectroscopy
202511 papers
- TNNLS (T1)A Virtual-Label-based Hierarchical Domain Adaptation Method for Time-Series Classification (opens in a new tab)
- Proceedings of the Conference on Language ModelingBackdoor Attacks on Dense Retrieval via Public and Unintentional Triggers (opens in a new tab)
- ICML (T1)Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms (opens in a new tab)
- theGLID2E: A Gradient-Free Lightweight Fine-tune Approach for Discrete Sequence Design
- NeurIPS (T1)GLID2E: Lightweight Policy-Based Fine-Tuning for Discrete Diffusion in Biological Sequence Design (opens in a new tab)
- ICML (T1)KVTuner: Sensitivity-Aware Layer-Wise Mixed Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference (opens in a new tab)
- theLarge Reasoning Models Know How to Think Efficiently
- NeurIPS (T1)MetaDefense: Defending Fine-tuning based Jailbreak Attack Before and During Generation (opens in a new tab)
- ICLR (T1)Parameter and Memory Efficient Pretraining via Low-rank Riemannian Optimization (opens in a new tab)
- ICLR (T1)Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation (opens in a new tab)
- Findings of the Annual Meeting of the Association for Computational LinguisticsReinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
202412 papers
- AAAI (T1)A Diffusion Model with State Estimation for Degradation-blind Inverse Imaging (opens in a new tab)
- TOISCollaborative Sequential Recommendations via Multi-View GNN-Transformers (opens in a new tab)
- ACMLConvergence Analysis of Inexact Over-relaxed ADMM via Dissipativity Theory (opens in a new tab)
- Proceedings of the Conference on Language ModelingDoes In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning (opens in a new tab)
- TPAMI (T1)Fast Graph Generation via Spectral Diffusion (opens in a new tab)
- WWW (T1)Graph Principal Flow Network for Conditional Graph Generation (opens in a new tab)
- ICRA (T1)Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners (opens in a new tab)
- ICLR (T1)Learning Adaptive Multiresolution Transforms via Meta-Framelet-based Graph Convolutional Network (opens in a new tab)
- ICLR (T1)Multilingual Jailbreak Challenges in Large Language Models (opens in a new tab)
- IEEE Transactions on Instrumentation and MeasurementOvercoming Negative Transfer by Online Selection: A Distant Domain Adaptation Method for Fault Diagnosis
- Findings of the Annual Conference of the North American Chapter of the Association for Computational LinguisticsSentiment Analysis in the Era of Large Language Models: A Reality Check (opens in a new tab)
- NeurIPS (T1)State Chrono Representation for Enhancing Generalization in Reinforcement Learning (opens in a new tab)
202310 papers
- EMNLP (T1)Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning (opens in a new tab)
- ACL (T1)Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (opens in a new tab)
- TPAMI (T1)Clustered Task-Aware Meta-Learning by Learning from Learning Paths (opens in a new tab)
- theConditional Graph Generation with Graph Principal Flow Network
- ICCV (T1)Deep Multitask Learning with Progressive Parameter Sharing (opens in a new tab)
- ICDE (T1)Distribution-Regularized Federated Learning on Non-IID Data (opens in a new tab)
- EMNLP (T1)Pages 8704-8711
- ICML (T1)Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint (opens in a new tab)
- NeurIPS (T1)Retaining Beneficial Information from Detrimental Data for Neural Network Repair (opens in a new tab)
- theVariational Component Decoder for Source Extraction from Nonlinear Mixture
Where alumni went14 listed
Apply to CUHKFall 2027
Computer Science and Engineering MPhil/PhD
- Round
- HKPFS / main round
- Opens
- —
- GRE
- See program page
- English
- Interview in English
- Fee
- See program page
Not yet confirmed for this intake.
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