• Qiu, L., et al. [“Reinforcement Learning via On-Policy Self-Distillation”]

  • Qiu, L., et al. [“Towards Better Visual Credit Assignment in Multimodal Reinforcement Learning”]

  • Qiu, L., et al. [“DUEL: Adversarial Self-Play for Multimodal Reasoning”][arXiv]

  • Diyi Hu, Ruizhong Qiu, Lin Qiu, Hanghang Tong, Hanqing Zeng. [“ProbePO: Sparse Probes, Redistributed Credit for Critic-Free LLM Reinforcement Learning”] (NeurIPS 2026)

  • Qinhong Zhou, Riley Zong, Chunru Lin, Shivraj Singh Bhatti, Hongxin Zhang, Diyi Hu, Lin Qiu, Hanqing Zeng, Chuang Gan. [“HARP-Bench: Benchmarking Robot Manipulation Around Active Humans”] (CoRL 2026)

  • Qiu, L., et al. [“Variational Multi-view Learning”] (AISTATS 2026)

  • Qiu, L., et al. [“Deep Pathology Genomic Multimodal Survival Prediction”][arXiv] (Nature Machine Intelligence Under Revision)

  • Wu,M.H., Littman,R., Levine,J., Qiu, L., Biancalani, T., Richmond,D., Huetter, JC. [“Contextualizing biological perturbation experiments through language”] (ICLR 2025) [arXiv]

  • Qiu, L., et al. [“Predicting Celluar Response from Interpretable Causal Inference”] (NeurIPS 2024) [arXiv]

  • Qiu, L., Chinchilli, V. M., and Lin, L. (2022). [“Interpretable Deep Representation Learning from Temporal Multi-view Data”]. (ACML 2022 Long Oral) [arXiv][Github]

  • Qiu, L., Lin, L., and Chinchilli, V. M. (2022). [“Variational Interpretable Deep Canonical Correlation Analysis”]. (ICLR 2022) [arXiv][Slides]

  • Qiu, L., Llerena, N. L., Sousa, V. S., Lin, L., and Chinchilli, V. M. (2022). [“Probabilistic Model Incorporating Auxiliary Covariates to Control FDR”]. (CIKM 2022) [acm][arxiv][Github]

  • Qiu, L., Sousa, V. S., and Llerena, N. L. (2022). “Visual Tag Emerging Pattern Detection”. US Patent App. 17/120, 933, 2022.

  • Qiu, L., and Chinchilli, V. M. (2022). [“Probabilistic Canonical Correlation Analysis for High-dimensional Sparse Count Data”]. Journal of Statistical Research, 56(1), 75–100.[paper][Github]