PEER-REVIEWED PUBLICATIONS
16. G. Gao, Q. Gao, X. Yang, S. Ju, M. Pajic and M. Chi. On Trajectory Augmentations for Off-Policy Evaluation. Accepted to International Conference on Learning Representations (ICLR) 2024.
15. Q. Gao, G. Gao, J. Dong, V. Tarokh, M. Chi and M. Pajic. Off-Policy Evaluation for Human Feedback. In proceedings of Neural Information Processing Systems (NeurIPS) 2023. [code in supplementary material]
14. S. L. Schmidt, A. H. Chowdhury, K. T. Mitchell, J. J. Peters, Q. Gao, H-J Lee, K. Genty, S-C Chow, W. M. Grill, M. Pajic, D. A. Turner. At Home Adaptive Dual Target Deep Brain Stimulation in Parkinson Disease with Proportional Control. In Brain 2023, awad429.
13. Q. Gao, G. Gao, M. Chi and M. Pajic. Variational Latent Branching Model for Off-Policy Evaluation. In proceedings of International Conference on Learning Representations (ICLR) 2023. [code]
12. Q. Gao, S. Schmidt, A. Chowdhury, G. Feng, J. J. Peters, K. Genty, W. M. Grill, D. A. Turner, M. Pajic. Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment. In proceedings of International Conference on Cyber-Physical Systems (ICCPS) 2023. Best paper award runner-up (2nd place) — top 2%.
11. G. Gao, Q. Gao, X. Yang, M. Pajic, and M. Chi. A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification. In proceedings of International Joint Conference of Artificial Intelligence (IJCAI) 2022.
10. Q. Gao, D. Wang, J. Amason, S. Yuan, C. Tao, R. Henao, M. Hadziahmetovic, L. Carin, M. Pajic. Gradient Importance Learning for Incomplete Observations. In proceedings of International Conference on Learning Representations (ICLR) 2022. [code]
9. Q. Gao, S. Schmidt, K. Kamaravelu, D. A. Turner, W. M. Grill, M. Pajic. Offline Policy Evaluation for Learning-based Deep Brain Stimulation Controllers. In proceedings of International Conference on Cyber-Physical Systems (ICCPS) 2022.
8. A. Khazraei, S. Hallyburton, Q. Gao, Y. Wang, M. Pajic. Learning-Based Vulnerability Analysis of Cyber-Physical Systems. In proceedings of International Conference on Cyber-Physical Systems (ICCPS) 2022.
7. T. Lee, M. Hu, Q. Gao, J. Amason, D. Borkar, D. D’Alessio, M. Canos, A. Shariff, M. Pajic, M. Hadziahmetovic. Evaluation of A Deep Learning Supported Remote Diagnosis Model for Identification of Diabetic Retinopathy using Wide-field Optomap. In Annals of Eye Science, Vol. 7, 2022.
6. Y. Wang, Q. Gao, M. Pajic. Learning Monotone Dynamics by Neural Networks. In proceedings of American Control Conference (ACC) 2022.
5. Q. Gao, J. Amason, S. W. Cousins, M. Pajic, and M. Hadziahmetovic. Automated Remote Diagnosis Tool for Multi-modal Identification of Retinal Pathology. In ARVO’s Journal on Translational Vision Science & Technology (TVST). Vol.10, 30. May 2021. [code]
4. Q. Gao, M. Pajic and M. M. Zavlanos. Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations. In proceedings of International Conference on Robotics and Automation (ICRA). June 2020. Paris, France.
3. Q. Gao, Y. Xu, J. Amason, A. Loksztejn, S. W. Cousins, M. Pajic, and M. Hadziahmetovic. Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples using Neural Networks. In ARVO’s Journal on Translational Vision Science & Technology (TVST). Vol.9, 31. June 2020.
2. Q. Gao, M. Naumann, I. Jovanov, V. Lesi, K. Kamaravelu, W. M. Grill, and M. Pajic. Model-Based Design of Closed Loop Deep Brain Stimulation Controller using Reinforcement Learning. In proceedings of International Conference on Cyber-Physical Systems (ICCPS). April 2020. Sydney, Australia.
1. Q. Gao, D. Hajinezhad, Y. Zhang, Y. Kantaros, and M. M. Zavlanos. Reduced Variance Deep Reinforcement Learning with Temporal Logic Specifications. In proceedings of International Conference on Cyber-Physical Systems (ICCPS). April 2019. Montreal, Canada.