PPaperPicks

Jian Liang

Chinese Academy of Sciences, Institute of Automation, Center for Research on Intelligent Perception and Computing, State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Beijing, China

20 papers at tracked venues · 19 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning
  2. What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time
  3. Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models
  4. Cooperative Pseudo Labeling for Unsupervised Federated Classification
  5. Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
  6. Exploring Vacant Classes in Label-Skewed Federated Learning
  7. LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
  8. LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
  9. Protecting Model Adaptation from Trojans in the Unlabeled Data
  10. R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
  11. The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models
  12. Uni-Layout: Integrating Human Feedback in Unified Layout Generation and Evaluation
  13. A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
  14. Backdoor Defense via Test-Time Detecting and Repairing
  15. Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models
  16. Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation
  17. Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
    ICML 2024 · Jian Liang
  18. STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
  19. Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
  20. Towards Reliable Model Selection for Unsupervised Domain Adaptation: An Empirical Study and A Certified Baseline